IAB Transparency & Consent Framework

Transparency & Consent Framework, Interactive Advertising Bureau (IAB)

Purposes

  • Accessing a device allow storing or accessing information on a user’s device.
  • Advertising personalisation allow processing of a user’s data to provide and inform personalised advertising (including delivery, measurement, and reporting) based on a user’s preferences or interests known or inferred from data collected across multiple sites, apps, or devices; and/or accessing or storing information on devices for that purpose
  • Analytics allow processing of a user’s data to deliver content or advertisements and measure the delivery of such content or advertisements, extract insights and generate reports to understand service usage; and/or accessing or storing information on devices for that purpose.
  • Content personalisation allow processing of a user’s data to provide and inform personalised content (including delivery, measurement, and reporting) based on a user’s preferences or interests known or inferred from data collected across multiple sites, apps, or devices; and/or accessing or storing information on devices for that purpose.

Features

  • Matching data to offline sources combining data from offline sources that were initially collected in other contexts.
  • Linking devices allow processing of a user’s data to connect such user across multiple devices.
    Precise geographic location data allow processing of a user’s precisegeographic location data in support of a purpose for which that certain third party has consent.

Purpose versus Feature

  • Purpose is a data use that drives a specific business model and produces specific outcomes for consumers and businesses. Purposes must be itemised at the point of collection, either individually or combined.
  • Feature is a method of data use or data sourcing that overlaps across multiple purposes. Features must be disclosed at the point of collection, but can be itemised separately to cover multiple purposes.

Promotional

Attack of the Zombie Web Sites, owned by 301 Network, Monkey Frog, Market 57, Orange Box, Arceneaux, Becks, AdSupply, Focus Marketing, Lepton Labs, Willis, Corson, VivaGlam, RecipeGreen, Van Derham | BuzzFeed

Attack of the Zombie Websites; Craig Silverman; In BuzzFeed; 2017-10-17.
Teaser: <snip>actual reporting, by an actual reporter</snip> how seemingly-credible players in the ad supply chain can play an active role in — and profit from — fraud.

Accused

Whereas the article buries the lede way way down under the fold…
  • 301network Media, allied “dbas”; Matt Arceneaux, Andrew Becks.
    Monkey Frog Media, Market 57, Orange Box Media
  • AdSupply, allied “dbas”; Eric Willis, Chris Corson.
    Focus Marketing, Lepton Labs
  • KVD Brand Inc.; Katarina Van Derham.

Original Sources

  • Social Puncher, an research boutique, operated as SadBotTrue.com.
  • Pixelate, opined; claims independent discovery.
  • Protected Media, opined, as commissioned, from BuzzFeed.
  • Integral Ad Sciences (IAS), opined, as commissioned, from BuzzFeed..

Mentions

  • “self-driven”
  • “session hijacking”
  • “friend or foe” system
  • “ad hell”
  • <quote>It was the digital equivalent of skimming from a casino.</quote>
  • “Clawbacks”
  • “In-human traffic,” “non-human traffic”
    because nobody in the trade wants to say “robot.”

Claimed

The Offenses
  • “Approximately” 40 websites.
  • “Over” 100 brands [what's a brand?]
  • “roughly” 50 brands “appeared multiple times.” [what does that mean?]
The Tease
  • <quote>the CEO of an ad platform and digital marketing agency is an owner of 12 websites that earned revenue from the fraudulent views, and his company provided the ad platform used by sites in the scheme.</quote>
  • <quote>That company is owned by a model and online entrepreneur who played Bob Saget’s girlfriend on the HBO show Entourage.</quote>
  • <quote><snip/>a former employee of a large ad network who runs a group of eight sites that were part of the fraud, and who consults for a company with another eight sites in it.</quote>
  • <quote>A site in the scheme is owned by the cofounder of one of the 20 largest ad networks in the United States</quote>.

Participants

  • 301network, a marketplace (“an ad platform”) and allied “dbas”;
    Matt Arceneaux, Andrew Becks.
  • AdSupply, various “dbas”;
    Eric Willis, Chris Corson.
  • KVD Brand Inc.;
    Katarina Van Derham.
Perpetrators
  • Matt Arceneaux, CEO, partner, 301 Digital Media.
  • Andrew Becks, COO, partner, 301 Digital Media
  • Eric Willis, vice president, OMG LLC
    is a man,
    ex-staff AdSupply,
    LinkedIn.
  • Chris Corson, founder of AdSupply,
    is part owner of an [unnamed] LLC that operates Hollywire.com
  • Katarina Van Derham,
    • is a publisher,
      is an online publisher,
    • lives in Los Angeles,
    • has performed as a model
    • has fame,
      has fame from playing Bob Saget’s girlfriend on the HBO show Entourage.
    • owns KVD Brand Inc.
301 Digital Media
  • 301network.com
  • a marketing agency
  • Nashville, TN
  • LinkedIn page [existed]
    clients:

    • Scripps
    • Pfizer
  • gold-level sponsor, Digital Marketing Conference, New York, 2017-11.

Damaged

  • Integral Ad Sciences (IAS) → $20 million in 2017.
  • Pixelate → $2 million per year.”
    Which is it?

The Validation

Re-checking the work of the Social Puncher staff
  • Integral Ad Science (IaS)

Exemplars

Businesses
  • Ford
  • Hershey’s
  • Johnson & Johnson
  • MGM Resorts International
  • Proctor & Gamble (P&G)
  • Unilever
Brands
  • Charmin
  • Olay
  • Oral-B
  • Orgullosa
    [is that really a brand? yes. Spanish, translation proud
    <quote>Orgullosa is for women who don't settle for walking the same path, but instead make a new one every day.</quote> <quote ref="presser">P&G’s Orgullosa Launches the Nueva Latina Campaign to Celebrate and Showcase the Unique Experience of the Bicultural, Modern Latina </quote>]
  • Secret

Who

  • Matt Arceneaux, CEO, 301 Digital Media
    listed as a perpetrator.

Quoted

For color, background & verisimilitude…
  • Amin Bandeali, the CTO of Pixalate
  • Shailin Dhar, (now) founder, Method Media Intelligence.
    Method Media Intelligence is a research boutique
  • Mary Hynes, director of corporate communication, MGM International
  • Kristin Lemkau, chief marketing officer, JPMorgan Chase.
  • Jalal Nasir, CEO, Pixalate.
  • Maria Pousa, chief marketing officer, Integral Ad Sciences (IAS).
  • Marc Pritchard, chief brand officer, Proctor & Gamble (P&G)
    honorific: the consumer products giant
  • Vlad Shevtsov, director of investigations, Social Puncher
  • David Taylor, CEO, Proctor & Gamble (P&G).
  • Mike Zaneis, CEO, Trustworthy Accountability Group

Supply

The location of the fraud
  • BeautyTips.online
    well, there’s your problem… the TLD online just feels sketchy, doesn’t it?
  • BridalTune.com
  • GossipFamily.com
  • HealthyBackyard.com
  • MensTrait.com
  • MomTaxi.com
  • RecipeGreen.com
    • uses automated [robot] content generation scheme
      “100% Fully Automated Videos – You won’t have to worry about new content. Comes with a custom plugin with your own license,” via blurb at Flippa,
    • 2016-12, purchased by Katarina Van Derham, for $59 in an auction
    • 2017-01 → 2017-08, was “showered” with traffic, then none.
  • RightParent.com
  • StyleFashionista.com
  • UpcomingBeauty.com
  • VivaGlamMagazine.com
    • branded Viva Glam,
    • operated by Katarina Van Derham since 2012,
    • not purchased from Pakistan or elsewhere.

Scenario

  • Monkey Frog Media LLC.
    • is a shell company [a holding company]
    • exposed for fraud “at seven sites”, by Pixelate [WHEN?]
    • Owned by Matt Arceneaux
    • d.b.a. Happy Planet Media
    • Has five more web sites
      whose domains are registered as being owned by 301 Digital Media, which is [owned?] by Matt Arceneaux
    • earlier [WHEN?] Matt Arcenaux’s home address for registration.
    • since 2015; as evidenced by 2015-12-11, Matt Arceneaux signs a contract as the “manager” of Monkey Frog Media.
  • Market 57 LLC
    • which had five sites
    • Same asddress as 301 Media
    • failing
    • ViralNewsJunkie.com, uses 301 Media’s Amazon affiliate code
    • earlier [WHEN?] Matt Arcenaux’s home address for registration.
  • Orange Box Media LLC
    • owns five sites
    • filing
    • uses Matt Arcenaux’s home address.
    • Observed by the Social Puncher staff: at circa 2017-09-08T12:00 EDT, all sites were unavailable simultaneously
  • Something about Facebook.
    Facebook is bad.
  • AppNexus was trading 301 Network Media’s media.
  • Online Media Group LLC (OMG LLC)
    • A shell company [a holding company]
    • owns seven sites
    • ran session hijacking code
    • Eric Willis, vice president, OMG LLC
      is a man.
  • AdSupply, seemed clean, maybe;
    but:

    • domains@adsupply.com.
    • Chris Corson, cofounder, executive vice president, AdSupply.
    • Chris Corson, is part owner of an LLC that operates Hollywire.com, a site that contained [the] session hijacking code.
    • Hollywire.com
      • is longstanding
      • produces some original content
      • has a YouTube channel, “close to” 2 million subscribers.
    • Focus Marketing. LLC,
      Chris Corson is the part owner.
    • Lepton Labs LLC,
      • purveyors of AllDaySlim, a weight-loss elixr.
    • Chris Corson is the part owner.
  • KVD Brand Inc.
    • eight sites
    • performed in the session hijacking scheme.
    • owned by Katarina Van Derham
    • bought the sites & their business from “someone in Pakistan.”
    • RecipeGreen.com
      • uses automated [robot] content generation scheme
        “100% Fully Automated Videos – You won’t have to worry about new content. Comes with a custom plugin with your own license,” via blurb at Flippa,
      • 2016-12, purchased by Katarina Van Derham, for $59 in an auction
      • 2017-01 → 2017-08, was “showered” with traffic, then none.

Referenced

Hosted at archive.is

  • Something, of 301network.com
  • Something, maybe an article, from StyleFashionista.com.
  • Something, maybe a “website,” of Focus Marketing. LLC
  • Something, maybe a “website,” of OMG LLC (Online Media Group, LLC)
  • Something, maybe a “product page,” for AllDaySlim, a weight-loss elixr.

Hosted at web.archive.org

  • Media Kit of www.301digitalmedia.com, as archived circa 2015-02-17T06:33:42.

Hosted at tnbear.tn.gov

Hosted on dropbox.com

Hosted on documentcloud.org

Previously filled.

Exploring ADINT: Using Ad Targeting for Surveillance on a Budget — or — How Alice Can Buy Ads to Track Bob | Vines, Roesner, Kohno

Paul Vines, Franziska Roesner, Tadayoshi Kohno; Exploring ADINT: Using Ad Targeting for Surveillance on a Budget — or — How Alice Can Buy Ads to Track Bob; In Proceedings of the 16th ACM Workshop on Privacy in the Electronic Society (WPES 2017); 2017-10-30; 11 pages; outreach.

tl;dr → Tadayoshi et al. are virtuosos at these performance art happenings. Catchy hook, cool marketing name (ADINT) and press outreach frontrunning the actual conference venue. For the wuffie and the lulz. Nice demo tho.
and → They bought geofence campaigns in a grid. They used close-the-loop analytics to identify the sojourn trail of the target.
and → dont’ use Grindr.

Abstract

The online advertising ecosystem is built upon the ability of advertising networks to know properties about users (e.g., their interests or physical locations) and deliver targeted ads based on those properties. Much of the privacy debate around online advertising has focused on the harvesting of these properties by the advertising networks. In this work, we explore the following question: can third-parties use the purchasing of ads to extract private information about individuals? We find that the answer is yes. For example, in a case study with an archetypal advertising network, we find that — for $1000 USD — we can track the location of individuals who are using apps served by that advertising network, as well as infer whether they are using potentially sensitive applications (e.g., certain religious or sexuality-related apps). We also conduct a broad survey of other ad networks and assess their risks to similar attacks. We then step back and explore the implications of our findings.

Mentions

  • Markets
    They chose

    • Facebooik
    • not Google
    • etc.
    • not to fight with big DSPs;
      the picked the weaker ones to highlight.
  • Apps
    They chose

    • lower-quality apps.
    • adult apps
      few “family oriented” [none?] apps.
    • <ahem>Adult Diapering Diary</ahem>
      <ahem>Adult Diapering Diary</ahem>

Claimed

  • DSPs sell 8m CEP (precision) location.

Spooky Cool Military Lingo

  • SIGINT
  • HUMINT
  • ADINT

Targeting Dimensions

  • Demographics
  • Interests
  • Personally-Identifying Information (PII)
  • Domain (a usage taxonomy)
  • Location
  • Identifiers
    • Cookie Identifier
    • Mobile Ad Identifier (e.g. IDFA, GPSAID)
  • Technographics
    • Device (Make Model OS)
    • Network (Carrier)
  • Search

Media Types

Supply-Side Platforms (SSPs)

  • Adbund
  • InnerActive
  • MobFox
  • Smaato
  • Xapas

Supply (the adware itself, The Applications, The Apps)

  • Adult Diapering Diary
  • BitTorrent
  • FrostWire
  • Grindr
  • Hide My Texts
  • Hide Pictures vault
  • Hornet
  • iFunny
  • Imgur
  • Jack’D
  • Meet24
  • MeetMe
  • Moco
  • My Mixtapez Music
  • Pregnant Mommy’s Maternity
  • Psiphon
  • Quran Reciters
  • Romeo
  • Tagged
  • Talkatone
  • TextFree
  • TextMe
  • TextPlus
  • The Chive
  • uTorrent
  • Wapa
  • Words with Friends

Demand-Side Platforms (DSPs)

  • Ademedo
  • AddRoll
  • AdWords
  • Bing
  • Bonadza
  • BluAgile
  • Centro
  • Choozle
  • Criteo
  • ExactDrive
  • Facebook
  • GetIntent
  • Go2Mobi
  • LiquidM
  • MediaMath
  • MightyHive
  • Simpli.Fi
  • SiteScout
  • Splicky
  • Tapad

Promotions

References

  • Gunes Acar, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayanan, Claudia Diaz. 2014. The Web Never Forgets: Persistent Tracking Mechanisms in the Wild. In Proceedings of the ACM Conference on Computer and Communications Security.
  • Rebecca Balebako, Pedro Leon, Richard Shay, Blase Ur, Yang Wang, L Cranor. 2012. Measuring the effectiveness of privacy tools for limiting behavioral advertising. In Web 2.0 Security and Privacy.
  • Hal Berghel. 2001. Caustic Cookies. In His Blog.
  • Interactive Advertising Bureau. 2015. IAB Tech Lab Content Taxonomy.
  • Interactive Advertising Bureau. 2017. IAB Interactive Advertising Wiki.
  • Giuseppe Cattaneo, Giancarlo De Maio, Pompeo Faruolo, Umberto Ferraro Petrillo. 2013. A review of security attacks on the GSM standard. In Information and Communication Technology-EurAsia Conference. Springer, pages 507–512.
  • Robert M Clark. 2013. Perspectives on Intelligence Collection. In The intelligencer, a Journal of US Intelligence Studies 20, 2, pages 47–53.
  • David Cole. 2014. We kill people based on metadata. In The New York Review of Books
  • Jonathan Crussell, Ryan Stevens, Hao Chen. 2014. Madfraud: Investigating ad fraud in android applications. In Proceedings of the 12th Annual International Conference on Mobile Systems, Applications, and Services. ACM, pages 123–134.
  • Doug DePerry, Tom Ritter, Andrew Rahimi. 2013. Cloning with a Compromised CDMA Femtocell.
  • Google Developers. 2017. Google Ads.
  • Steven Englehardt and Arvind Narayanan. 2016. Online tracking: A 1-million-site measurement and analysis. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, pages 1388–1401.
  • Steven Englehardt, Dillon Reisman, Christian Eubank, Peter Zimmerman, Jonathan Mayer, Arvind Narayanan, Edward W Felten. 2015. Cookies that give you away: The surveillance implications of web tracking. In Proceedings of the 24th International Conference on World Wide Web. ACM, pages 289–299.
  • Go2mobi. 2017.
  • Aleksandra Korolova. 2010. Privacy violations using microtargeted ads: A case study. In Proceedings of the 2010 IEEE International Conference on IEEE Data Mining Workshops (ICDMW), pages 474–482.
  • Zhou Li, Kehuan Zhang, Yinglian Xie, Fang Yu, XiaoFeng Wang. 2012. Knowing your enemy: understanding and detecting malicious web advertising. In Proceedings of the 2012 ACM conference on Computer and Communications Security. ACM, pages 674–686.
  • Nicolas Lidzborski. 2014. Staying at the forefront of email security and reliability: HTTPS-only and 99.978 percent availability.; In Their Blog. Google.
  • Steve Mansfield-Devine. 2015. When advertising turns nasty. In Network Security 11, pages 5–8.
  • Jeffrey Meisner. 2014. Advancing our encryption and transparency efforts. In Their Blog, Microsoft.
  • Rick Noack. 2014. Could using gay dating app Grindr get you arrested in Egypt?. In The Washington Post.
  • Franziska Roesner, Tadayoshi Kohno, David Wetherall. 2012. Detecting and Defending Against Third-Party Tracking on the Web. In Proceedings of the USENIX Symposium on Networked Systems Design and Implementation (NSDI).
  • Sooel Son, Daehyeok Kim, Vitaly Shmatikov. 2016. What mobile ads know about mobile users. In Proceedings of the 23rd Annual Network and Distributed System Security Symposium (NDSS).
  • Mark Joseph Stern. 2016. This Daily Beast Grindr Stunt Is Sleazy, Dangerous, and Wildly Unethical. In Slate, 2016.
  • Ryan Stevens, Clint Gibler, Jon Crussell, Jeremy Erickson, Hao Chen. 2012. Investigating user privacy in android ad libraries. In Proceedings of the Workshop on Mobile Security Technologies<e/m> (MoST).
  • Ratko Vidakovic. 2013. The Mechanics Of Real-Time Bidding. In Marketingland.
  • Craig E. Wills and Can Tatar. 2012. Understanding what they do with what they know. In Proceedings of the ACM Workshop on Privacy in the Electronic Society (WPES).
  • Tom Yeh, Tsung-Hsiang Chang, Robert C Miller. 2009. Sikuli: using GUI screenshots for search and automation. In Proceedings of the 22nd annual ACM Symposium on User Interface Software and Technology. ACM, pages 183–192.
  • Apostolis Zarras, Alexandros Kapravelos, Gianluca Stringhini, Thorsten Holz, Christopher Kruegel, Giovanni Vigna. 2014. The dark alleys of madison avenue: Understanding malicious advertisements. In Proceedings of the 2014 Conference on Internet Measurement Conference
  • Tiliang Zhang, Hua Zhang, Fei Gao. 2013. A Malicious Advertising Detection Scheme Based on the Depth of URL Strategy. In Proceedings of the 2013 Sixth International Symposium on Computational Intelligence and Design (ISCID), Vol. 2. IEEE, pages 57–60.
  • Peter Thomas Zimmerman. 2015. Measuring privacy, security, and censorship through the utilization of online advertising exchanges. Technical Report. Tech. rep., Princeton University.

Argot

The Suitcase Words

  • Mobile Advertising ID (MAID)
  • Demand-Side Platform (DSP)
  • Supply-Side Platform (SSP)
  • Global Positioning System (GPS)
  • Google Play Store (GPS)
  • geofencing
  • cookie tracking
  • Google Advertising Identifier (GAID)
    Google Play Services Advertising Identifier (GAID)
  • Facebook
  • Snowden
  • WiFi

Previously filled.

As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing? | Ad Week

As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing?; ; In Ad Week (Advertising Week); 2017-09-24.
Teaser: Acquisition of The Weather Company fuels a new division

tl;dr → Watson (a service bureau, AI-as-a-Service) is open for business.

Mentions

The Weather Company

  • lines of business
    • location-based targeted audiences, delivered to the trade.
    • weather indica, delivered to consumers.
  • 2.2 billion locations/15 minutes
  • Dates
    • WHEN?, Acquisition by IBM
    • 2016-01, new business strategy,
      “AI” as a service (AIaaS)
  • Artificial Intelligence (AI)
  • Cloud Computing
  • Products
    • WeatherFx
    • JourneyFx
  • The Weather Company is a <quote>legacy business<quote> (deprecated).
  • AIaaS is a <quote>cutting-edge advertising powerhouse</quote> (house of power).

Watson Advertising

  • Cognitive Advertising
    • contra Computational Advertising, circa the ‘oughties (2005)
    • something about
      • <buzzzz>transform every aspect of marketing from </buzzz>
      • something about image and voice recognition to big data analysis and custom content.
  • What is it? (What is Watson-as-a-Service?)
    • Count: <quote>dozens</quote>
    • Interfaces
      • API
      • Projects <quote>studio-like</quote>
    • Pricing: <quote>millions of dollars</quote>
    • Structure: four (4) sub-units
  • “<snip/>It’s not been designed to target consumers the same way that Alexa or Siri have been,” attributed to Cameron Clayton.

Units

The 4 pillars of Watson Advertising.
  1. Targeting, Audience construction & activation
  2. Optimization, Bidding & buying
  3. Advertising, Synthesis of copy and creative
  4. Planning, media planning, the buy plan, the execution plan

Audience Targeting

  • the flagship service
  • neural networks
  • scoring users, propensity scoring <quote>based on how likely they are to take an action</quote>
  • towards CPA or CCPV or CPVisit or <more!>
  • Performable on the Weather Company O&O
    • <quote>but on TV, print, radio and other platforms. <quote>
    • Partnerships
      • Cognitiv
      • Equals 3

Optimization

Bidding Optimization
  • Is too boring for details early in the article.
  • Optimize against brand-specific KPIs.
  • Uses <buzzz>deep learning and neural networks</buzzz>
  • Optimize Cost Per Action (CPA).

Advertising

  • Badged as Watson Ads and Watson Advertising
  • Services
    • content creation
    • content copywriting
  • Launched: 2016-06.
  • Is merely: nterest-Based Advertising (IBA)
    which in turn is a but regulatory term of art, that covers a wide range of in-trade practices.
  • Sectors, aspirational
    • <fancy>aviation</fancy> (airline ticket booking?)
    • insurance
    • energy
    • finance
  • Cognitive Media Council,
    • a focus group.
    • a user group, “friends & family” of the business.
    • a group of important customers representatives
      <quote>senior-level executives from agencies and brands</quote>
Reference Customers
Toyota
  • Mirai
  • Prius Prime
  • Benefits
    Attributed to Eunice Kim, Toyota (TMNA), something about…

    • <buzzz>create a one-to-one conversational engagement</buzzz>
    • <buzzz>garner insights about the consumer thought process that could potentially inform our communication strategies elsewhere”</buzzz>
Campbell’s
  • the Soup people
  • Something about creative synthesis
    themed as: recipe generation with flu symptoms with location
H&R Block
  • Something about creative synthesis
    themed as: automated robot tax expert, suggest tax deductions.
UM [You and Em]
  • An agency. Off shore? They have a “U.S. CEO” Maybe one of those English Invasion thingies.
  • Refused to name their client.
  • Something about auto dealerships.
  • <quote>meshing Watson data with client stats to analyze metrics across a large number of car dealerships in a way that optimizes ad spend while also checking local inventory to see whether or not it should personalize an ad to someone in that market.<quote>
  • <quote>combination of weather data, Google searches and pollen counts to trigger when media should be bought in various markets.</quote>

Planning

  • <quote>AI-powered planning</quote>

Partners

Cognitiv
Something about a partnership for understanding marketing texts.
Jeremy Fain, CEO and co-founder
Equals 3
Lucy, a product-service-platform.
Something about <quote>to uncover extra insights and research.<quote>

Fairness & Balance

Promotions

Ogilvy & Mather
  • Honorific <quote>longtime agency<quote> [fof record for IBM].
Stunts
2011
Jeopardy
2015
[Television] campaign, with Bob Dylan.
2016
Synthesis of the trailier for Morgan (a move; genre: science fiction)
2017-02
Performance, an “analysis” of the stylings of Antoni Gaudi, <quote>inspire an art installation </quote> (what does that mean?)
The “art installation” was exhibited at the Mobile World Congress in Barcelona.
Statista

…is quoted
the future is boosted.

Sectors
  • “AI services”
  • “Big Data services”

Themes

Problem
  • The people are “afraid” of AI.
  • The people need to be groomed to accept AI.
Remediation

Ensmoothen & enpitchen the Artificial Intelligence (AI) as…

  • humble
  • friendly
  • ”I’m here to help’ type personality”

Attributed to Lou Aversano, Ogilvy.

Detractors

James Kisner, Jeffries

Via: James Kisner, A Report, Jeffries, 2017-07.
Jeffries is an opinion vendor in support of an M&A banking operation.
tl;dr → Watson is a failing product-service. <quote>IBM is being “outgunned” in the race…</quote> (yup, he mixed the metaphor).

  • as evidenced in measured job listings at Monster.com
    Apple had more listings booked thereon than IBM.
  • Customers were interviewed.
    Watson’s performance/price ratio was low (the rate card is very high).
    2016-10, IBM reduced the rate card for API access <quote>by 70 percent</quote>
  • Lots of press
  • Not a lot of monetary results, as evidenced in the quarterly & annual reports.
Joe Stanhope, Gartner

Via: an interview, perhaps;
Gartner Group is an opinion vendor.

  • Too much hype, can be forgiven
  • Gartner runs the Hype Cycle brand
  • Claims: <quote>IBM does seem to be all-in with Watson.<quote> (be nice to hear that from IBM, not as a “hot-take” from a newshour pundit
DemandBase, Wakefield Research

A Report; attributed to “staff”; DemandBase and Wakefield Research

  • A survey,
    • “how do you feel?”
    • Do you “have plans-to …” in the next N months.
  • There are a lot of uncertainties

Uncertainties

Training Data
  • Just isn’t there.
  • And … computers can only give answers, it can’t give [pose] questions.
Does it [even] Work?
  • No one knows.
  • Many are nervous.
  • No one wants to be first to fail
    (& be fired for outsourcing their job function to The AI).

Competitors

  • Einstein, of Salesforce(.com)
  • Sensei, of Adobe
In-House
  • Buying operations, Xaxis of WPP
    the “AI” is a “co-pilot” to the trading desk operator; optimization recommendations towards CPM and viewability; North American operations only.
  • others?
    Surely everyone nowadays has some initiative that does “co-pilot”-level decision support to adops.
Research Efforts
  • Amazon
  • Facebook
  • Google
Venture Capital
  • Albert
  • Amenity Analytics
  • LiftIgniter
  • Persado
Amenity Analytics

An exemplar of the smaller-nimbler-smarter clones of the Watson genre.

  • A Watson-type experience, but cheaper
  • Does text mining of press releases
  • Reference customers:
    Pepsi
  • A spin-out from some hedge fund, <quote>origins in the hedge fund world</quote>
  • Nathaniel Storch, CEO, Amenity Analytics.
  • <zing!>“Think of it as ‘moneyball’ for media companies,”<zing!>, attributed to Nathaniel Storch.

Consumer

  • Siri, of Apple
  • Cortana, of Microsoft
  • Now, of Google

Who

  • Lou Aversano, U.S. CEO, Ogilvy & Mather (Ogilvy, O&M).
  • Jordan Bitterman, CMO, Watson (Business Unit), IBM.
    attributed in quoted material aso “earlier this year” (2017?); c.f. Michael Mendenhall
  • Kasha Cacy, U.S. CEO, UM
    UM is an agency.
  • Cameron Clayton,
    • General Manager, Content and IoT Platform, Watson (Business Unit), IBM..
    • ex-CEO, The Weather Company
  • Jacob Colker, “entrepreneur in residence,” The Allen Institute
    …quoted for color, background & verisimilitude.The Allen Institute is a tank for thinkers.
  • Jeremy Fain, CEO and co-founder, Cognitiv.
  • Chris Jacob, director of product marketing, Marketing Cloud, Salesforce(.com).
  • Eunice Kim, media planner, Toyota Motor North America (TMNA).
    …quoted for color, background & verisimilitude.
  • James Kisner, staff, Jeffries.
    …quoted for color, background & verisimilitude.
    Jeffries is an advice shop, like Gartner, but different.
  • Francesco Marconi,
    …quoted for color, background & verisimilitude.

    • strategy manager and AI co-lead, Associated Press
    • visitor, MIT Media Lab
  • Michael Mendenhall, CMO, Watson (BU), IBM.
    announced as CMO in prior press [Ad Week, Marty Swant, 2017-07-07].
  • Sara Robertson, VP of Product Engineering, Xaxis of WPP.
  • Joe Stanhope, staff, Forrester
    …quoted for color, background & verisimilitude.
  • Nathaniel Storch, CEO, Amenity Analytics.
  • Marty Wetherall, director of innovation, FallonFallon is the agency that certain campaign booked on Watson for H&R Block

Pantheon

  • Antoni Gaudi, architect (per civil engineering), citizen of Spain.

Previously

In archaeological order, within Advertising Week

Previously filled.

Syllabus for Solon Barocas @ Cornell | INFO 4270: Ethics and Policy in Data Science

INFO 4270 – Ethics and Policy in Data Science
Instructor: Solon Barocas
Venue: Cornell University

Syllabus

Solon Barocas

Readings

A Canon, The Canon

In order of appearance in the syllabus, without the course cadence markers…

  • Danah Boyd and Kate Crawford, Critical Questions for Big Data; In <paywalled>Information, Communication & Society,Volume 15, Issue 5 (A decade in Internet time: the dynamics of the Internet and society); 2012; DOI:10.1080/1369118X.2012.678878</paywalled>
    Subtitle: Provocations for a cultural, technological, and scholarly phenomenon
  • Tal Zarsky, The Trouble with Algorithmic Decisions; In Science, Technology & Human Values, Vol 41, Issue 1, 2016 (2015-10-14); ResearchGate.
    Subtitle: An Analytic Road Map to Examine Efficiency and Fairness in Automated and Opaque Decision Making
  • Cathy O’Neil, Weapons of Math Destruction; Broadway Books; 2016-09-06; 290 pages, ASIN:B019B6VCLO: Kindle: $12, paper: 10+SHT.
  • Frank Pasquale, The Black Box Society: The Secret Algorithms That Control Money and Information; Harvard University Press; 2016-08-29; 320 pages; ASIN:0674970845: Kindle: $10, paper: $13+SHT.
  • Executive Office of the President, President Barack Obama, Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights; The White House Office of Science and Technology Policy (OSTP); 2016-05; 29 pages; archives.
  • Lisa Gitelman (editor), “Raw Data” is an Oxymoron; Series: Infrastructures; The MIT Press; 2013-01-25; 192 pages; ASIN:B00HCW7H0A: Kindle: $20, paper: $18+SHT.
    Lisa Gitelman, Virginia Jackson; Introduction (6 pages)
  • Agre, “Surveillance and Capture: Two Models of Privacy”
  • Bowker and Star, Sorting Things Out
  • Auerbach, “The Stupidity of Computers”
  • Moor, “What is Computer Ethics?”
  • Hand, “Deconstructing Statistical Questions”
  • O’Neil, On Being a Data Skeptic
  • Domingos, “A Few Useful Things to Know About Machine Learning”
  • Luca, Kleinberg, and Mullainathan, “Algorithms Need Managers, Too”
  • Friedman and Nissenbaum, “Bias in Computer Systems”
  • Lerman, “Big Data and Its Exclusions”
  • Hand, “Classifier Technology and the Illusion of Progress” [Sections 3 and 4]
  • Pager and Shepherd, “The Sociology of Discrimination: Racial Discrimination in Employment, Housing, Credit, and Consumer Markets”
  • Goodman, “Economic Models of (Algorithmic) Discrimination”
  • Hardt, “How Big Data Is Unfair”
  • Barocas and Selbst, “Big Data’s Disparate Impact” [Parts I and II]
  • Gandy, “It’s Discrimination, Stupid”
  • Dwork and Mulligan, “It’s Not Privacy, and It’s Not Fair”
  • Sandvig, Hamilton, Karahalios, and Langbort, “Auditing Algorithms: Research Methods for Detecting Discrimination on Internet Platforms”
  • Diakopoulos, “Algorithmic Accountability: Journalistic Investigation of Computational Power Structures”
  • Lavergne and Mullainathan, “Are Emily and Greg more Employable than Lakisha and Jamal?”
  • Sweeney, “Discrimination in Online Ad Delivery”
  • Datta, Tschantz, and Datta, “Automated Experiments on Ad Privacy Settings”
  • Dwork, Hardt, Pitassi, Reingold, and Zemel, “Fairness Through Awareness”
  • Feldman, Friedler, Moeller, Scheidegger, and Venkatasubramanian, “Certifying and Removing Disparate Impact”
  • Žliobaitė and Custers, “Using Sensitive Personal Data May Be Necessary for Avoiding Discrimination in Data-Driven Decision Models”
  • Angwin, Larson, Mattu, and Kirchner, “Machine Bias”
  • Kleinberg, Mullainathan, and Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores”
  • Northpointe, COMPAS Risk Scales: Demonstrating Accuracy Equity and Predictive Parity
  • Chouldechova, “Fair Prediction with Disparate Impact”
  • Berk, Heidari, Jabbari, Kearns, and Roth, “Fairness in Criminal Justice Risk Assessments: The State of the Art”
  • Hardt, Price, and Srebro, “Equality of Opportunity in Supervised Learning”
  • Wattenberg, Viégas, and Hardt, “Attacking Discrimination with Smarter Machine Learning”
  • Friedler, Scheidegger, and Venkatasubramanian, “On the (Im)possibility of Fairness”
  • Tene and Polonetsky, “Taming the Golem: Challenges of Ethical Algorithmic Decision Making”
  • Lum and Isaac, “To Predict and Serve?”
  • Joseph, Kearns, Morgenstern, and Roth, “Fairness in Learning: Classic and Contextual Bandits”
  • Barocas, “Data Mining and the Discourse on Discrimination”
  • Grgić-Hlača, Zafar, Gummadi, and Weller, “The Case for Process Fairness in Learning: Feature Selection for Fair Decision Making”
  • Vedder, “KDD: The Challenge to Individualism”
  • Lippert-Rasmussen, “‘We Are All Different’: Statistical Discrimination and the Right to Be Treated as an Individual”
  • Schauer, Profiles, Probabilities, And Stereotypes
  • Caliskan, Bryson, and Narayanan, “Semantics Derived Automatically from Language Corpora Contain Human-like Biases”
  • Zhao, Wang, Yatskar, Ordonez, and Chang, “Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints”
  • Bolukbasi, Chang, Zou, Saligrama, and Kalai, “Man Is to Computer Programmer as Woman Is to Homemaker?”
  • Citron and Pasquale, “The Scored Society: Due Process for Automated Predictions”
  • Ananny and Crawford, “Seeing without Knowing”
  • de Vries, “Privacy, Due Process and the Computational Turn”
  • Zarsky, “Transparent Predictions”
  • Crawford and Schultz, “Big Data and Due Process”
  • Kroll, Huey, Barocas, Felten, Reidenberg, Robinson, and Yu, “Accountable Algorithms”
  • Bornstein, “Is Artificial Intelligence Permanently Inscrutable?”
  • Burrell, “How the Machine ‘Thinks’”
  • Lipton, “The Mythos of Model Interpretability”
  • Doshi-Velez and Kim, “Towards a Rigorous Science of Interpretable Machine Learning”
  • Hall, Phan, and Ambati, “Ideas on Interpreting Machine Learning”
  • Grimmelmann and Westreich, “Incomprehensible Discrimination”
  • Selbst and Barocas, “Regulating Inscrutable Systems”
  • Jones, “The Right to a Human in the Loop”
  • Edwards and Veale, “Slave to the Algorithm? Why a ‘Right to Explanation’ is Probably Not the Remedy You are Looking for”
  • Duhigg, “How Companies Learn Your Secrets”
  • Kosinski, Stillwell, and Graepel, “Private Traits and Attributes Are Predictable from Digital Records of Human Behavior”
  • Barocas and Nissenbaum, “Big Data’s End Run around Procedural Privacy Protections”
  • Chen, Fraiberger, Moakler, and Provost, “Enhancing Transparency and Control when Drawing Data-Driven Inferences about Individuals”
  • Robinson and Yu, Knowing the Score
  • Hurley and Adebayo, “Credit Scoring in the Era of Big Data”
  • Valentino-Devries, Singer-Vine, and Soltani, “Websites Vary Prices, Deals Based on Users’ Information”
  • The Council of Economic Advisers, Big Data and Differential Pricing
  • Hannak, Soeller, Lazer, Mislove, and Wilson, “Measuring Price Discrimination and Steering on E-commerce Web Sites”
  • Kochelek, “Data Mining and Antitrust”
  • Helveston, “Consumer Protection in the Age of Big Data”
  • Kolata, “New Gene Tests Pose a Threat to Insurers”
  • Swedloff, “Risk Classification’s Big Data (R)evolution”
  • Cooper, “Separation, Pooling, and Big Data”
  • Simon, “The Ideological Effects of Actuarial Practices”
  • Tufekci, “Engineering the Public”
  • Calo, “Digital Market Manipulation”
  • Kaptein and Eckles, “Selecting Effective Means to Any End”
  • Pariser, “Beware Online ‘Filter Bubbles’”
  • Gillespie, “The Relevance of Algorithms”
  • Buolamwini, “Algorithms Aren’t Racist. Your Skin Is just too Dark”
  • Hassein, “Against Black Inclusion in Facial Recognition”
  • Agüera y Arcas, Mitchell, and Todorov, “Physiognomy’s New Clothes”
  • Garvie, Bedoya, and Frankle, The Perpetual Line-Up
  • Wu and Zhang, “Automated Inference on Criminality using Face Images”
  • Haggerty, “Methodology as a Knife Fight”
    <snide>A metaphorical usage. Let hyperbole be your guide</snide>

Previously filled.

Marketing Technology Landscape Supergraphic (2017): Martech 5000


(ChiefMarTec); Marketing Technology Landscape Supergraphic (2017): Martech 5000; In Their Blog; 2017-05-10.

Occasion

Martech 5000
Slide
copy, original
JPEG
copy, original
PDF
copy, original
Sheet

Licentious licentiate: <quote ref=”cite“>Feel free to cut-and-paste this data and use it as a starting point for your own research.</quote>

Mentions

  • Integration-Platform-as-a-Service(iPaaS, IPaaS)
    • are “distributed” platforms
    • perform <quote>[as] dynamically piping data between marketing applications and [a] data lake.</quote>
  • Content Management System (CMS)
    • are platforms, per se
    • are centralized
    • are repositories of data and services
    • Gartner staff renamed them digital marketing hub
  • Among: DMP, CDP, RTIM
    • is a subtle blending among them
    • The Spectrum
      • Data Management Platforms (DMP),
      • Customer Data Platform (CDP),
      • Real-Time Interaction Management (RTIM)

Exemplars

Content Management System (CMS)

  • Adobe
  • HubSpot
  • IBM
  • Marketo
  • Oracle
  • Salesforce
  • Sitecore

IPaaS, now with Microservices!

  • Boomi, a wholly-owned subsidiary of Dell, a.k.a. Dell Boomi
  • Informatica
  • Jitterbit
  • Mulesoft
  • Segment
  • Zapier

The Spectrum Among: DMP, CDP, RTIM

Customer Data Platform (CDP)
  • AgilOne
  • Lytics
  • RedPoint
  • Tealium
  • Treasure Data
  • Usermind
Data Management Platform (DMP)
As a feature, not even Line of Business
  • Adobe
  • Oracle
  • Salesforce
Standalone
  • DataXu
  • MediaMath
  • Neustar
  • Rocketfuel, (sic) Rocket Fuel of Sizmek
Real-Time Importance Management (RTIM)
  • Experian
    but not Acxiom? EXPM contra ACXM …”the same, but different” aren’t they?
  • Infor
  • Pegasystems
  • SAS
  • Teradata

Credited

Iterations

Argot

  • Content Management System (CMS)
  • Customer Data Platform (CDP)
  • Customer Relationship Management (CRM)
  • Data Lake, an inelegant metaphor,
    a body corpora of water data facts in a controlled-but-unstructured format.
  • Data Management Platform (DMP)
  • Digital Marketing Hub (DMH)
    Gartner ‘lingo for the MarTech genre.
  • Enabler
    Doesn’t actually do the work, but still sends a bill for allowing it to occur.
    Usage: <quote>iPaaS and microservice platform enablers.</quote>
  • Integration-Platform-as-a-Service (iPaaS)
  • Long Tail
  • Marketing Automation Platform (MAP)
  • Microservices
  • Platform-as-a-Service (iPaaS)
  • Real-Time Interaction Management (RTIM)
  • Service-as-a-Service (SaaS)
  • Success-as-a-Service, a scheme.
    e.g. 2&20.

Referenced

Previously

In Their Blog

Actualities

Licentiate: ibidem.




Pre-Conference AdTech Summarization | Gubbins

; Things you should know about AdTech, today; In His Blog, centrally hosted on LinkedIn; 2017-08-30; regwalled (you have to login to linkedin).

Occasion

Boosterism in front of the trade shows
  • Exchange Wire #ATSL17
  • Dmexco
  • Programmatic IO

Mentions

  • There be consolidation in the DSP category.
  • There will be more DSPs not less fewer.
  • Owned & Operated (O&O)
  • preferential deals
  • private equity companies
  • party data & a GDPR compliant screen agnostic ID
  • no “point solutions.”
  • Doubleclick Bid Manager (DBM), Google
  • Lara O’Reilly; Some Article; In Business Insider (maybe); WHEN?
    tl;dr → something about how Google DSP DBM guarantee “fraud-free” traffic.
  • Ads.txtAuthorized Digital Sellers, IAB Tech Lab
  • Claimed:
    comScore publishers are starting to adopt Ads.txt

Buy Side

Deal Flow
  • Sizmek acquired Rocket Fuel, (unverified) $145M.
  • Tremor sells its DSP to Taptica for $50M.
  • Singtel acquired Turn for $310M.
No flow, yet
  • Adform
  • MediaMath
  • DataXu
  • AppNexus

Sell Side

  • Header Bidding (HB)
    • Replaces the SSP category
    • <quote>effectively migrated the sell sides narrative & value prop of being a yield management partner to that of a feet on the street publisher re-seller.</quote>
  • QBR (Quarterly Business Result?)
  • Prebid.js
  • With server bidding, too.
  • Supply Path Optimization (SPO)
    • Brian O’Kelley (AppNexus); Article; In His Blog; WHEN?
      Brian O’Kelley, CEO, AppNexus.
    • Article; ; In ExchangeWire; WHEN?
  • Exchange Bidding in Dynamic Allocation (EBDA), Google
Exemplars
The Rubicon Project
a header tag, compatible with most wrappers, no proprietary wrapper, only Prebid.js
Index Exchange
a header tag, compatible with most wrappers, a proprietary wrapper
OpenX
a header tag that, compatible with many (not ‘most’) wrappers, a proprietary wrapper
AppNexus
a header, compatible with many (not ‘most’) wrappers, a proprietary wrapper (that is better than OpenX’s which is not enterprise grade)
PubMatic
a header tag, compatible with many (not ‘most)’ wrappers, a proprietary wrapper.
Other
  • TrustX
    • with
      • Digital Content Next
      • IPONWEB
      • ANA
    • Something about a transparent marketplace.
  • Something about another supply network
    • German
    • trade press in Digiday
Mobile
  • No header bidding, yet.
  • Mobile equals Adware (“in app”)
    • but Apps don’t have “browsers.”
    • but App browsers don’t have “pages” with “headers.”
    • though Apps have SDKs (libraries).
Video
  • RTL acquires SpotX
  • <quote>One could argue video is the perfect storm for header bidding, limited quality supply & maximum demand, the ideal conditions for a unified auction…</quote>
Talking Points
  • The industry is currently debating the pros & cons of running header bidding either client or server side (A lot boils down to latency V audience match rates)
  • Google offer their own version of header bidding, this is referred to as EBDA (Exchange Bidding in Dynamic Allocation) and is available to DFP customers.
  • Facebook recently entered header bidding by launching a header tag that enables publishers to capture FAN demand via header bidding on their mobile traffic.
  • Criteo entered header bidding by offering publishers their header tag (AKA Direct Bidder) that effectively delivers Criteos unique demand into the publisher’s header auction, at a 1st rather than cleared 2nd price.
  • Amazon have launched a server to server header bidding offering for publishers that delivers unique demand and the ability to manage other S2S demand partners for the publisher.
Extra Credit
  • <quote>senior AdTech big wigs</quote>
  • programmatic auction process
  • 1st v 2nd price
  • 2nd price was for waterfall
  • 1st price will be for unified (header bidding)

General Data Protection Regulation’ (GDPR)

  • 2018-05
  • Consent must be collected.
  • Will make 2nd party data marketplaces economical.
  • The salubrious effect.
  • Publishers have a Direct Relationship with consumers.
    this is argued as being “better.”
  • Industry choices
    • collect holistic consent
      <quote>one unified [process] of consumer [outreach] rather than one for every vendor</quote>
    • individual vendor consent
      <quote>for every cookie or device ID that flows through the OpenRTB pipes we have spent the last 10 years laying.</quote>

Viewability & Brand Safety

  • IAB
  • MRC

Talking Points

  • Moat was sold to Oracle for reported number of $800M.
  • PE Firm Providence Equity bought a % of Double Verify giving them a reported value of $300M.
  • Integral Ad Science remains independent, for now

Telcos

  • Telcos have what everybody in AdTech wants:
    • accurate data
    • privacy compliant data
    • scaled data
    • 1st party data.
  • Telcos want what AdTech & publishing companies have:
    • programmatic sell and buy side tools
    • content creation functions
    • distribution at scale.
    • diversification of revenues

Talking Points

  • Verizon buys AOL & Yahoo to form Oath, a publisher, a DSP, a DMP.
  • Telenor buys TapAd, a cross-device DMP-type-thing
  • Altice buys Teads, a streaming video vendor)
  • Singtel buys Turn, a DSP
  • AT&T needs a line in this list; might want to buy Time Warner which is a movie studio, media holding copmany, a cable operator, an old owner of AOL.
Shiny
Smartpipe
Raised $18.75M, Series A. Why?
ZeoTap
Raised $20M, through Series B, Why?

Data Management Platform (DMP)

  • Not a pure-play business.
    • A division, not a business.
    • An interface, not a division.
  • Everyone wants to own one.
Deciderata
  • Should DMP’s also be in the media buying business?
  • What are DMP’s doing to stay relevant for a world without cookies?
  • Do DMP’s plan to build or buy device graph features / functions?
  • For platforms that process & model a lot of 1st, 2nd & 3rd party data, how will they be affected by the pending GDPR?
Talking Points
  • Adobe bought Tube Mogul, a video DSP, for $540M (based on information &amp belief).
  • Oracle bought Moat, a verification feature, for $800M
  • Oracle bought Crosswise, a cross-device database, for <unstated/>
  • Salesforce bought Krux, a DMP, FOR $700M

Lotame remains independent, for now

ID Consortium’s & Cross-Device Players

Claims
Probabilistic “won’t work”
<quote>The GDPR may make it very difficult for a number of probabilistic methods to be applied to digital ID management.</quote>
Walled Garden
They … <quote>are using their own proprietary cross-screen deterministic token / people based ID that in many cases only works within their O&O environments.</quote>
Universal ID
Is desired. <quote>CMO’s & agencies in the future will not be requesting a cleaner supply chain, but a universal ID (or ID clearing house) that will enable them to manage reach, frequency & attribution across all of the partners they buy from.</quote>
Initiatives
The DigiTrust
<quote>This technology solution creates an anonymous user token, which is propagated by and between its members in lieu of billions of proprietary pixels and trackers on Web pages.</quote>
Claim: “Many” leading AdTech companies are already working with the DigiTrust team. [Which?]
AppNexus ID Consortium
  • Scheme: people-based ID.
  • Launch: 2017-05
  • Trade Name: TBD
    • Index Exchange
    • LiveRamp
    • OpenX
    • Live Intent
    • Rocket Fuel
Standalones
  • Adbrain
  • Screen6
  • Drawbridge

Blockchain

BUZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZZ!

  • Blockchain is slow, too slow, way too slow
    Blockchain can handle 10 tps.
  • Does not work in OpenRGB
  • NYIAX
    • New York City
Referenced
  • Some Q&A; In AdExchanger
    tl;dr → interview of Dr Boris WHO?, IPONWEB; self-styled “the smartest man in AdTech and he concurs”

Artificial Intelligence

  • Is bullshit.
  • c.f.(names dropped)
    • Deepmind
    • Boston Dynamics

Omitted

  • DOOH
  • Audio
  • Programmatic TV
  • Over The Top (OTT)
  • MarTech != AdTech

Previously filled.

Roundup of miscellaneous notes, captured and organized

Blockchain Culture

The Seven(Hundred) Dwarves

  • Blockstack(.org)- The New Decentralized Internet
    • blockstack, at GitHub
    • Union Square Ventures (USV)
    • Promotion
      • Staff (USV); The Blockchain App Stack; In Their Blog; 2016-08-08.
      • Blockstack Unveils A Browser For The Decentralized Web; Laura Shin; In Forbes; 2017-05-15.
        tl;dr → <quote>Tuesday, at the main blockchain industry conference, Consensus, one of the companies working on this new decentralized web, Blockstack, which has $5.5 million in funding from Union Square Ventures and AngelList cofounder Naval Ravikant, released a browser add-on that enables that and more.<snip/>The add-on enables a browser to store the user’s identity information by a local key on the consumer’s device.</quote>; Ryan Shea, cofounder.
  • Everyone has something here.

Bluetooth Culture

Bluetooth LE (BLE)

  • and?

Bluetooth 5

  • Something about mesh networking
  • Something about the standard being released “summer 2017.”

C++ Culture

C++20

  • The roadmap onto the twenties.

Application

  • MapReduce, from ETL or EU somewhere.
  • Kyoto Cabinet, Typhoon, Tycoon
  • Virtual Reality packages
  • Ctemplate, Olafud Spek (?)
  • Robot Operating System (ROS)
  • libgraphqlparser – A GraphQL query parser in C++ with C and C++ APIs

Computing Culture

Ubicomp, <ahem>Pervicomp</ahem>

  • Rich Gold
  • Mark Weiser

Dev(Ops) Culture

Futures Cult(ure)

Advocacy

  • Cory Doctorow, the coming war against general purpose computing, an article; WHERE?
  • Cory Doctorow, dystopia contra utopia, an article; WHERE?

Fiction

  • Cory Doctorow, various works

Imagine a World In Which…

  • Stocks vs Flows
  • Chaos vs Stability
  • Permission vs Permissionless
  • Civil Society ↔ Crony Society
    • Transparency
    • Deals
    • Priorities
  • Predictive Technology “just works”
    • is trusted
    • is eventual
    • is law
    • “is” equates with “ought”

Fedora Culture

  • Flatpak

Fedora 26 Notes

  • nmcli reload con down $i
  • nm cli reload con up $i
  • eui64 must be manually configured

Internet of (unpatchable) Thingies (IoT)

  • MQTT
  • mosquito

Language Lifestyles

Go Lang

  • Go for it.
  • A package manager

LangSec

  • theory
  • implementation?

Rust Lang

  • Was there a NoStarch book?

SCOLD Lang

  • C++20?
    hey, surely someone has modules working by now, eh?

Projects

Generally

  • Repig, in C++, with threads, in an NVMe

mod_profile

  • sure, what?

mod_proliphix

  • Interface to the (discontinued) Proliphix thermostats

mod_resting

  • CDN Store
  • Picture Store
  • Document Cache (store & forward)

mod_files

  • Firefox Tiles

SCOLD Experiences

SCOLD near-syntax, common errors

  • #import <hpp>
  • missing #divert
  • #using, a declaration
  • #origin
  • #namespace
  • $@

Suggestions

Build System
  • –with-std-scold or maybe –with-scold
module-c-string
  • vecdup, like strdup
  • vectree, like strfree→free
module-json
  • json::check::Failure or json::Cast.
  • namespace json::is
    • is_array
    • is_null
    • is_object
  • json::as<…>(…)
module-path
  • pathify(…)
module-sqlite
  • column result
  • concept guarding the template parameter, from C++17
module-string
  • typed strings
    • location
    • path
    • etc.
  • and

Surveillance Culture

Concepts

  • Eigenpeople
  • Eigenpersonas
  • Personality modeling

Literature

Yves-Alexandre de Montjoye, Jordi Quoidbach, Florent Robic, Alex (Sandy) Pentland; Predicting Personality Using Novel Mobile Phone-Based Metrics; In: A.M. Greenberg, W.G. Kennedy, N.D. Bos (editors) Social Computing, Behavioral-Cultural Modeling and Prediction as Proceedings of Social Computing, Behavioral (SBP 2013), Lecture Notes in Computer Science, vol 7812; 2013; paywalls: Springer, ACM. Previously filled.

Theory

  • POSS (Post Open Source Software)
    defined as: if everything is on GitHub, then who needs licenses?
    Was this ever amplified?
    Certainly it is facially incorrect and facile.

Psychology

  • Rob Horning; Sock of Myself, an essay; In Real Life Magazine; 2017-05-17
    tl;dr → riffing on happiness, Facebook. Is. Bad. Q.E.D. R.D. Laing , The Divided Self,; John Cheney-Lippold’s We Are Data; Donald Mackenzie.
  • Michael Nelson; University of California, Riverside.

Purposive directionality

  • increase
    • predictability
  • reduce
    • uncertainty
    • variability

Various

Uncomprehensible, Unknown, Unpossible

  • Sunlight, a package? FOSS?

The Marketer’s Guide To Blockchain | AdExchanger

The Marketer’s Guide To Blockchain; ; In AdExchanger; 2017-07-06.

Mentions

Backers
  • IBM
  • Comcast
Technology
  • MadHive
  • Rebel AI

Vehicles

AdLedger Consortium
Group

  • IBM,, anchor
  • Integral Ad Science, trading
  • MadHive, a boutique
  • TEGNA, subsidiary of Premion, a DSP for OTT

Scope: unclear

Comcast
Group

  • Comcast
  • Altice USA
  • Cox
  • NBCUniversal
  • Disney

Scope: data share contracts, record transactions in blockchain.
Story: <quote>
a vetted, trusted media buyer could execute a campaign against segments provided by members of the Comcast consortium</quote>

NYIAX
Only: NASDAQ
Scope: sells guarantee contracts as futures; not live inventory.

  • TV ad buying
  • multiple stakeholders
  • multi-party contracts
Interactive Advertising Bureau (IAB)
“in an exploratory phase”, attributed to Allana Gompert.

Tense

All future tense. Very aspirational. Many qualifiers.

AdLedger
Qualifiers: Still forming, working group, will dictate policy, will dictate API specifications.
Comcast
Qualifiers: Not until 2018 [e-o-2018] is “2019,” think: ~600 days
NYIAX
Qualifiers: is developing, proofs of concept, beta partners.

Benefits

  • <quote>securely share their assets without exporting or handing them over to another stakeholder </quote>
  • <quote>And media owners can strike a blow against unauthorized sellers and domain spoofers. </quote>
  • <quote>[remove out unwanted supply chain intermediaries </quote>

Method

  • Use [unique] blockchain keys instead of gimmicky [URL] names
  • Use blockchain <snip/> to log transactions, recording the use of “data”consumer dossiers.

Story

  • <paraphrase>[As] Comcast and Cox, [I have] different inventory rates for specific content or audiences, or [as a] buyer [I] wants to blacklist certain supply sources, <snip> each company’s smart contract and dictates how others on the blockchain can access its data. <paraphrase>
  • <paraphrase>[As an] advertiser [I] could lock up inventory over the long term and publishers could score bigger upfront deals or offer different types of discounts. And in this instance, blockchain would serve as the ledger recording all of these transactions – and their value. </paraphrase>

Problems

  • Slow
  • Does. Not. Scale.
  • Ill-posed
    • The media business wants transparency.
    • The media business requires opacity.
Solutions
  • AppNexus
  • DoubleClick Ad Exchange

Quoted

  • Ken Brook, CEO, MetaX, a boutique
  • Peter Guglielmino, CTO, IBM’s Media & Entertainment Group.
  • Alanna Gombert, general manager of the IAB Tech Lab.
  • Adam Helfgott, founder, MadHive, a boutique
  • Will Luttrell, Curren-C, a boutique; co-founder, ex-former CTO, Integral Ad Science
  • Manny Puentes, CEO, Rebel AI, a boutique
  • Lou Severine, CEO, NYIAX CEO

Soup

  • government-backed legal tender
  • secured bank vaults,
  • bitcoin
  • blockchain ledger
  • guarantee security
  • full transparency
  • smart contracts

Previously

In Ad Exchanger

Listicle

MadHive
AdLedger Consortium, in data trading around OTT
Rebel AI
Something about “hoping to develop”, something about brand safety & ad fraud
MetaX
On-(block-)chain and off-chain solutions. adChain, a protocol on Ethereum. with Direct Marketing Association Data & Marketing Association (DMA)
Comcast
“like legacy data players” Acxiom and Experian; Blockchain Insights Platform; not before 2019.
NYIAX
Is NASDAQ’s proprietary blockchain; a futures recordation scheme, against The Upfronts. Among ex-AOL VP-levels, Bill Wise, founder and CEO, Mediaocean, is a board member
IBM
Bluemix, a services suite, cloud-blockchain frontrunner. Something about having deep pockets, being able to incur long periods of R&D costs, hoping to recoup on IBM services in other domains, e.g. healthcare and finance.
Curren-C
A boutique.

Previously filled.

Networks of Control | Cracked Labs

!


Wolfie Christl and Sarah Spiekermann; Networks of Control; Facultas, Vienna; 2016; 185 pages; landing.
Teaser: A Report on Corporate Surveillance, Digital Tracking, Big Data & Privacy

Table of Contents

  1. Preface
  2. Introduction
  3. Analyzing Personal Data
    1. Big Data and predicting behavior with statistics and data mining
    2. Predictive analytics based on personal data: selected examples
      1. The “Target” example: predicting pregnancy from purchase behavior
      2. Predicting sensitive personal attributes from Facebook Likes
      3. Judging personality from phone logs and Facebook data
      4. Analyzing anonymous website visitors and their web searches
      5. Recognizing emotions from keyboard typing patterns
      6. Forecasting future movements based on phone data
      7. Predicting romantic relations and job success from Facebook data
    3. De-anonymization and re-identification
  4. Analyzing Personal Data in Marketing, Finance, Insurance and Work
    1. Practical examples of predicting personality from digital records
    2. Credit scoring and personal finance
    3. Employee monitoring, hiring and workforce analytics
    4. Insurance and healthcare
    5. Fraud prevention and risk management
    6. Personalized price discrimination in e-commerce
  5. Recording Personal Data – Devices and Platforms
    1. Smartphones, mobile devices and apps – spies in your pocket?
    2. Car telematics, tracking-based insurance and the Connected Car
      1. Data abuse by apps
    3. Wearables, fitness trackers and health apps – measuring the self
      1. A step aside – gamification, surveillance and influence on behavior
      2. Example: Fitbit’s devices and apps
      3. Transmitting data to third parties
      4. Health data for insurances and corporate wellness
    4. Ubiquitous surveillance in an Internet of Things?
      1. Examples – from body and home to work and public space
  6. Data Brokers and the Business of Personal Data
    1. The marketing data economy and the value of personal data
    2. Thoughts on a ‘Customers’ Lifetime Risk’ – an excursus
    3. From marketing data to credit scoring and fraud detection
    4. Observing, inferring, modeling and scoring people
    5. Data brokers and online data management platforms
    6. Cross-device tracking and linking user profiles with hidden identifiers
    7. Case studies and example companies
      1. Acxiom – the world’s largest commercial database on consumers
      2. Oracle and their consumer data brokers Bluekai and Datalogix
      3. Experian – expanding from credit scoring to consumer data
      4. arvato Bertelsmann – credit scoring and consumer data in Germany
      5. LexisNexis and ID Analytics – scoring, identity, fraud and credit risks
      6. Palantir – data analytics for national security, banks and insurers
      7. Alliant Data and Analytics IQ – payment data and consumer scores
      8. Lotame – an online data management platform (DMP)
      9. Drawbridge – tracking and recognizing people across devices
      10. Flurry, InMobi and Sense Networks – mobile and location data
      11. Adyen, PAY.ON and others – payment and fraud detection
      12. MasterCard – fraud scoring and marketing data
  7. Summary of Findings and Discussion of its Societal Implications
    1. Ubiquitous data collection
    2. A loss of contextual integrity
    3. The transparency issue
    4. Power imbalances
    5. Power imbalances abused: systematic discrimination and sorting
    6. Companies hurt consumers and themselves
    7. Long term effects: the end of dignity?
    8. Final reflection: From voluntary to mandatory surveillance?
  8. Ethical Reflections on Personal Data Markets (by Sarah Spiekermann)
    1. A short Utilitarian reflection on personal data markets
    2. A short deontological reflection on personal data markets
    3. A short virtue ethical reflection on personal data markets
    4. Conclusion on ethical reflections
  9. Recommended Action
    1. Short- and medium term aspects of regulation
    2. Enforcing transparency from outside the “black boxes”
    3. Knowledge, awareness and education on a broad scale
    4. A technical and legal model for a privacy-friendly digital economy
  10. List of tables
  11. List of figures
  12. References

Mentions

yes

Quoted

  • Anna Fielder, Chair of Privacy International
  • Courtney gabrielson, International Association of Privacy Professionals (IAPP)

References

There are 677 footnoes, which are distinct from the references.
There are 211 references.

Separately filled.

Corporate Surveillance in Everyday Life | Cracked Labs


Corporate Surveillance in Everyday Life. How Companies Collect, Combine, Analyze, Trade, and Use Personal Data on BillionsWolfie Christl,; Cracked Labs, Vienna; 2017-06; 93 pages.

Teaser: <shrill>How thousands of companies monitor, analyze, and influence the lives of billions. Who are the main players in today’s digital tracking? What can they infer from our purchases, phone calls, web searches, and Facebook likes? How do online platforms, tech companies, and data brokers collect, trade, and make use of personal data?</shrill>

Table of Contents

  1. Background and Scope
  2. Introduction
  3. Relevant players within the business of personal data
    1. Businesses in all industries
    2. Media organizations and digital publishers
    3. Telecom companies and Internet Service Providers
    4. Devices and Internet of Things
    5. Financial services and insurance
    6. Public sector and key societal domains
    7. Future developments?
  4. The Risk Data Industry
    1. Rating people in finance, insurance and employment
    2. Credit scoring based on digital behavioral data
    3. Identity verification and fraud prevention
    4. Online identity and fraud scoring in real-time
    5. Investigating consumers based on digital records
  5. The Marketing Data Industry
    1. Sorting and ranking consumers for marketing
    2. The rise of programmatic advertising technology
    3. Connecting offline and online data
    4. Recording and managing behaviors in real-time
    5. Collecting identities and identity resolution
    6. Managing consumers with CRM, CIAM and MDM
  6. Examples of Consumer Data Broker Ecosystems
    1. Acxiom, its services, data providers, and partners
    2. Oracle as a consumer data platform
    3. Examples of data collected by Acxiom and Oracle
  7. Key Developments in Recent Years
    1. Networks of digital tracking and profiling
    2. Large-scale aggregation and linking of identifiers
    3. “Anonymous” recognition
    4. Analyzing, categorizing, rating and ranking people
    5. Real-time monitoring of behavioral data streams
    6. Mass personalization
    7. Testing and experimenting on people
    8. Mission creep – everyday life, risk assessment and marketing
  8. Conclusion
  9. Figures
  10. References

Mentions

Quoted

  • Omer Tene
  • Jules Polonetsky

Promotions

Yes.  A work this polished could be hid for long.

Summary

The web variant is summary material.

  1. Analyzing people
  2. Analyzing people in finance, insurance and healthcare
  3. Large-scale collection and use of consumer data
  4. Data brokers and the business of personal data
  5. Real-time monitoring of behaviors across everyday life
  6. Linking, matching and combining digital profiles
  7. Managing consumers and behaviors, personalization and testing
  8. Dragnet – everyday life, marketing data and risk analytics
  9. Mapping the commercial tracking and profiling landscape
  10. Towards a society of pervasive digital social control?

References

There are 601 footnotes, which are distinct from the references.
There are 102 of references

Previously filled.

De-Anonymizing Web Browsing Data with Social Networks | Su, Shukla, Goel, Narayanan

Jessica Su, Ansh Shukla, Sharad Goel, Arvind Narayanan; De-Anonymizing Web Browsing Data with Social Networks; draft; In Some Venue Surely (they will publish this somewhere, it is so very nicely formatted); 2017-05; 9 pages.

Abstract

Can online trackers and network adversaries de-anonymize web browsing data readily available to them? We show—theoretically, via simulation, and through experiments on real user data—that de-identified web browsing histories can be linked to social media profiles using only publicly available data. Our approach is based on a simple observation: each person has a distinctive social network, and thus the set of links appearing in one’s feed is unique. Assuming users visit links in their feed with higher probability than a random user, browsing histories contain tell-tale marks of identity. We formalize this intuition by specifying a model of web browsing behavior and then deriving the maximum likelihood estimate of a user’s social profile. We evaluate this strategy on simulated browsing histories, and show that given a history with 30 links originating from Twitter, we can deduce the corresponding Twitter profile more than 50% of the time. To gauge the real-world e↵ectiveness of this approach, we recruited nearly 400 people to donate their web browsing histories, and we were able to correctly identify more than 70% of them. We further show that several online trackers are embedded on sufficiently many websites to carry out this attack with high accuracy. Our theoretical contribution applies to any type of transactional data and is robust to noisy observations, generalizing a wide range of previous de-anonymization attacks. Finally, since our attack attempts to find the correct Twitter profile out of over 300 million candidates, it is—to our knowledge—the largest-scale demonstrated de-anonymization to date.

Mentions

yes.

Quotes

  • <quote>Network adversaries—including government surveillance agencies, Internet service providers, and co↵ee shop eavesdroppers—also see URLs of unencrypted web traffic. The adversary may also be a cross-device tracking company aiming to link two di↵erent browsing histories (e.g., histories generated by the same user on di↵erent devices). For such an adversary, linking to social media profiles is a stepping stone.</quote>

Headline

374 people confirmed the accuracy of our deanonymization attempt.
268 people (72%) were the top candidate generated by the MLE when using t.co links.
303 people (81%) were among the top 15 candidates generated by the MLE when using t.co links.
Yet only 49% de-anonymization when using fully expanded links (the redirect target of the t.co link)
Background

<paraphrasing>We recruited participants by advertising the experiment on a variety of websites, including

  • Twitter,
  • Facebook,
  • Quora,
  • Hacker News,
  • Freedom to Tinker
Story Line
649
people submitted web browsing histories.
119 cases (18%)
the application encountered a fatal error (e.g., because the Twitter API was temporarily unavailable), and it was unable to run the de-anonymization algorithm.
530 cases
remaining are useful.

87 users (16%)
had fewer than four informative links, and so no attempt to de-anonymize them was made.
443 users
remaining are useful.

374 users (84%)
confirmed whether or not our de-anonymization attempt was successful.
77 users (21%),/dt>
additionally disclosed their identity by signing into Twitter.

Apology: noted that the users who participated in our experiment are not representative of the Twitter population. In particular, they are quite active: the users who reported their identity had a median number of 378 followers and posted a median number of 2,041 total tweets.

</paraphrasing>

Framing (Environment)

  • TargetConsumer is a Registered Twitter User,
    with activity and warm content selection algo in operation at Twitter HQ
  • Twitter algo selects snippets for presentation to TargetConsumer.
  • TargetConsumer either elects to read or discards the linked page.
  • An URL trail is recorded by The Panopticon Surveillance Machinery in The Record
  • Adversary has access to The Record across long spans of time and large numbers of TargetConsumers.

Problem Statement

  • Can one or many TargetConsumers be distinguished solely by URL traces in The Record?

Algorithm (Conceptual)

See C. Y. Ma, D. K. Yau, N. K. Yip, N. S. Rao. “Privacy vulnerability of published anonymous mobility traces,” In IEEE/ACM Transactions on Networking, 21(3):720–733, 2013.
<paraphrasing>

  1. The simple model of web browsing behavior in which a user’s likelihood of visiting a URL is governed by the URL’s overall popularity and whether the URL appeared in the TargetConsumer’s Twitter feed.
  2. For each TargetConsumer, we compute their likelihood (under the model) of generating a given anonymous browsing history.
  3. Identify the TargetConsumer most likely to have generated that history.

</paraphrasing>

Argot

  • Cookie Syncing
  • E-Tag
  • HTML5 localStorage
  • HTTP (HTTP)
  • Jaccard Similarity
  • Maximum Liklihood Estimate (MLE)
  • URL (URL)

Promotions

  • Ad Networks Can Personally Identify Web Users; Wendy Davis; In MediaPost; 2017-01-20.
    <quote> The authors tested their theory by recruiting 400 people who allowed their Web browsing histories to be tracked, and then comparing the sites they visited to sites mentioned in Twitter accounts they followed. The researchers say they were able to use that method to identify more than 70% of the volunteers.</quote>

References

  • G. Acar, C. Eubank, S. Englehardt, M. Juarez, A. Narayanan, C. Diaz. The web never forgets: Persistent tracking mechanisms in the wild. In Proceedings of ACM Conference on Computer Communications & Security (CCS), pages 674–689. ACM, 2014.
  • G. Acar, M. Juarez, N. Nikiforakis, C. Diaz, S. Gürses, F. Piessens, B. Preneel. Fpdetective: dusting the web for fingerprinters. In Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security (CCS), pages 1129–1140. ACM, 2013.
  • M. D. Ayenson, D. J. Wambach, A. Soltani, N. Good, C. J. Hoofnagle. Flash cookies and privacy II: Now with HTML5 and ETag respawning. 2011.
  • C. Budak, S. Goel, J. Rao, G. Zervas. Understanding emerging threats to online advertising. In Proceedings of the ACM Conference on Economics and Computation, 2016.
  • M. Chew, S. Stamm. Contextual identity: Freedom to be all your selves. In Proceedings of the Workshop on Web,/em>, volume 2. Citeseer, 2013.
  • ] N. Christin, S. S. Yanagihara, K. Kamataki. Dissecting one click frauds. In Proceedings of the 17th ACM conference on Computer and Communications Security
  • Y.-A. De Montjoye, C. A. Hidalgo, M. Verleysen, V. D. Blondel. Unique in the crowd: The privacy bounds of human mobility. In Scientific Reports, 3, 2013.
  • Y.-A. De Montjoye, L. Radaelli, V. K. Singh, et al. Unique in the shopping mall: On the reidentifiability of credit card metadata. In Science, 347(6221), 2015.
  • P. Eckersley. How unique is your web browser? In, pages 1–18. Springer, 2010.
  • S. Englehardt, A. Narayanan. Online tracking: A 1-million-site measurement and analysis. In Proceedings of the ACM Conference on Computer and Communications Security (CCS), 2016.
  • S. Englehardt, D. Reisman, C. Eubank, P. Zimmerman, J. Mayer, A. Narayanan, E. W. Felten. Cookies that give you away: The surveillance implications of web tracking. In Proceedings of the 24th Conference on World Wide Web (WWW), 2015.
  • Ú. Erlingsson, V. Pihur, A. Korolova. Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the Conference on Computer and Communications Security (CCS), 2014.
  • D. Fifield, S. Egelman. Fingerprinting web users through font metrics. In Proceedings of the International Conference on Financial Cryptography and Data Security, 2015.
  • S. Hill, F. Provost. The myth of the double-blind review?: Author identification using only citations. In SIGKDD Explor(ification) Newsletter, 5(2):179–184, Dec. 2003.
  • M. Korayem, D. J. Crandall. De-anonymizing users across heterogeneous social computing platforms. In Proceedings of the Internation Conference on W(something) S(something) M(something) as “Some Acronym” (ICWSM), 2013.
  • A. Korolova, K. Kenthapadi, N. Mishra, A. Ntoulas. Releasing search queries and clicks privately. In Proceedings of the 18th International Conference on World Wide Web (WWW). ACM, 2009.
  • B. Krishnamurthy, K. Naryshkin, C. Wills. Privacy leakage vs. protection measures: the growing disconnect. In Proceedings of the Web
  • B. Krishnamurthy, C. E. Wills. On the leakage of personally identifiable information via online social networks. In Proceedings of the 2nd ACM Workshop on Online Social Networks (WOSN), pages 7–12. ACM, 2009.
  • P. Laperdrix, W. Rudametkin, B. Baudry. Beauty and the beast: Diverting modern web browsers to build unique browser fingerprints. In Proceedings of the 37th IEEE Symposium on Security and Privacy, 2016.
  • A. Lerner, A. K. Simpson, T. Kohno, F. Roesner. Internet jones and the raiders of the lost trackers: An archaeological study of web tracking from 1996 to 2016. In Proceedings of the 25th USENIX Security Symposium, 2016.
  • T. Libert. Exposing the invisible web: An analysis of third-party http requests on 1 million websites. In International Journal of Communication, 9:18, 2015.
  • C. Y. Ma, D. K. Yau, N. K. Yip, N. S. Rao. Privacy vulnerability of published anonymous mobility traces. In IEEE/ACM Transactions on Networking, 21(3):720–733, 2013.
  • A. Marthews, C. Tucker. Government surveillance and internet search behavior. Available at ssrn:2412564, 2015.
  • N. Mathewson, R. Dingledine. Practical traffic analysis: Extending and resisting statistical disclosure. In Proceedings of the International Workshop on Privacy Enhancing Technologies (PETS), pages 17–34. Springer, 2004.
  • J. R. Mayer, J. C. Mitchell. Third-party web tracking: Policy and technology. In Proceedings of the 2012 IEEE Symposium on Security and Privacy. IEEE, 2012.
  • K. Mowery, H. Shacham. Pixel perfect: Fingerprinting canvas in HTML5. In Proceedings of the Conference with the Acronym “W2SP” (W2SP), 2012.
  • A. Narayanan, H. Paskov, N. Z. Gong, J. Bethencourt, E. Stefanov, E. C. R. Shin, D. Song. On the feasibility of internet-scale author identification. In Proceedings of the IEEE Symposium on Security and Privacy, 2012.
  • A. Narayanan, V. Shmatikov. Robust de-anonymization of large sparse datasets. In Proceedings of the 2008 IEEE Symposium on Security and Privacy (SP), pages 111–125. IEEE, 2008.
  • N. Nikiforakis, A. Kapravelos, W. Joosen, C. Kruegel, F. Piessens, G. Vigna. Cookieless monster: Exploring the ecosystem of web-based device fingerprinting. In Proceedings of the 2013 IEEE symposium on Security and Privacy (SP), pages 541–555. IEEE, 2013.
  • L. Olejnik, G. Acar, C. Castelluccia, C. Diaz. The leaking battery A privacy analysis of the HTML5 Battery Status API. Technical Report, WHERE? 2015.
  • L. Olejnik, C. Castelluccia, A. Janc. Why Johnny can’t browse in peace: On the uniqueness of web browsing history patterns. In Proceedings of the 5th Workshop on Hot Topics in Privacy Enhancing Technologies (PETS), 2012.
  • J. Penney. Chilling effects: Online surveillance and wikipedia use. In Berkeley Technology Law Journal, 2016.
  • A. Ramachandran, Y. Kim, A. Chaintreau. “I knew they clicked when I saw them with their friends”. In Proceedings of the 2nd Conference on Online Social Networks, 2014.
  • F. Roesner, T. Kohno, D. Wetherall. Detecting and defending against third-party tracking on the web. In Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, pages 12–12. USENIX Association, 2012.
  • K. Sharad, G. Danezis. An automated social graph de-anonymization technique. In Proceedings of the 13th Workshop on Privacy in the Electronic Society (WPES), pages 47–58. ACM, 2014.
  • A. Soltani, S. Canty, Q. Mayo, L. Thomas, C. J. Hoofnagle. Flash cookies and privacy. In Proceedings of the AAAI Spring Symposium: Intelligent Information Privacy Management, volume 2010, pages 158–163, 2010.
  • J. Su, A. Sharma, S. Goel. The effect of recommendations on network structure. In Proceedings of the 25th Conference on World Wide Web (WWW), 2016.
  • G. Wondracek, T. Holz, E. Kirda, C. Kruegel. A practical attack to de-anonymize social network users. In Proceedings of the IEEE Symposium on Security and Privacy, 2010.

Previously filled.

Big Data, Psychological Profiling and the Future of Digital Marketing | Sandra Matz

Sandra Matz; Digital Psychometrics and its Future Effects on Technology; 34 slides.

Talks

  • Sandra Matz; Digital Psychometrics and its Future Effects on Technology; Keynote at ApacheCon; 2017-05-16; video: 23:08.
  • Sandra Matz; Big Data, Psychological Profiling and the Future of Digital Marketing; President’s Lecture, at The Berlin School; On YouTube; 2017-02-20; video: 1:10:52.

Mentions

  • www.sandramatz.com
  • www.psychometrics.cam.ac.uk
  • www.discovermyprofile.com
  • Cambridge Analytica
  • Apply Magic Sauce, Prediction API
  • myPersonality Project
    • myPersonality Database

Psychometrics

  • Personality (Big Five, OCEAN)
  • Values
  • Life Satisfaction
  • Impulsivity
Personality
  • Openness to experience
  • Conscientiousness
  • Extraversion
  • Agreeableness
  • Neuroticism

Sources

Background

Actualities

Referenced

Is Facebook Targeting Ads at Sad Teens?

      ;

Michael Reilly

      ; In

MIT Technology Review

      ; 2017-05-01.
      Teaser:

The social network appears to leverage sensitive user data to aim ads at teenagers who say they feel “anxious” and “worthless.”

Online Privacy and ISPs | Institute for Information Security & Privacy, Georgia Tech

Peter Swire, Justin Hennings, Alana Kirkland; Online Privacy and ISPs; a whitepaper; Institute for Information Security & Privacy, Georgia Tech; 2016-05; 131 pages.
Teaser: ISP Access to Consumer Data is Limited and Often Less than Access by Others

Authors
  • Peter Swire
    • Associate Director,
      The Institute for Information
      Security & Privacy at Georgia Tech
    • Huang Professor of Law,
      Georgia Tech Scheller College of Business
      Senior Counsel, Alston & Bird LLP
  • Justin Hemmings,
    • Research Associate,
      Georgia Tech Scheller College of Business
    • Policy Analyst
      Alston & Bird LLP
  • Alana Kirkland
    • Associate Attorney, Alston & Bird LLP

tl;dr → ISP < Media; ISPs are not omnipotent; ISPs see less than you think; Consumer visibility is mitigated by allowed usage patterns: cross-ISP, cross-device, VPN, DNS obfuscation, encryption.  Anyway, Facebook has it all and more.

Consumer profiling observation is already occurring by other means anyway.

<quote> In summary, based on a factual analysis of today’s Internet ecosystem in the United States, ISPs have neither comprehensive nor unique access to information about users’ online activity. Rather, the most commercially valuable information about online users, which can be used for targeted advertising and other purposes, is coming from other contexts. Market leaders are combining these contexts for insight into a wide range of activity on each device and across devices. </quote>

<translation> The other guys are already doing it, why stop ISPs? </translation>

ISP surveillanceObservation of consumers is neither Comprehensive, nor Unique

<quote> The Working Paper addresses two fundamental points. First, ISP access to user data is not comprehensive – technological developments place substantial limits on ISPs’ visibility. Second, ISP access to user data is not unique – other companies often have access to more information and a wider range of user information than ISPs. Policy decisions about possible privacy regulation of ISPs should be made based on an accurate understanding of these facts. </quote>

<view> It’s unargued why comprehensive or unique are bright-line standards of anything at all. </view>

Previously filled.

Mentions

Claims

  • ISPs < Media
    The dumb-pipe, bit-shoving, ISPs see less than media services, who see semantic richness.
  • Cross-device is the new nowadays.
  • Encryption is everywhere.

Definitions

Availability
  • a technical statement
  • contra “use” which is an action by a person
Cross-Device Tracking
Deterministic
Logged-In, Cross-Context Tracking
Probabilistic
Not Logged-In, Cross-Context Tracking
Cross-Device Tracking
  • Frequency Capping
  • Attribution
  • Improved Advertising Targeting
  • Sequenced Advertising
  • Tracking Simultaneity
Limits the use of “data” (facts about consumers)
  • at the point of collection
  • at the point of use
Location of a consumer
  • Coarse contra Precise
  • Current contra Historical

Summary

The document has both a Preface and an Executive Summary. so the journeyperson junior policy wonkmaker can approach the material at whatever level of complexity their time budget and training affords.

Preface

  • Technological Developments Place Substantial Limits on ISPs’ Visibility into Users’ Online Activity:
    1. From a single stationary device to multiple mobile devices and connections.
    2. Pervasive encryption.
    3. Shift in domain name lookup.
  • Non-ISPs Often Have Access to More and a Wider Range of User Information than ISPs:
    1. Non-ISP services have unique insights into user activity.
    2. Non-ISPs dominate in cross-context tracking.
    3. Non-ISPs dominate in cross-device tracking.

Executive Summary

  • Technological Developments Place Substantial Limits on ISPs’ Visibility into Users’ Online Activity:
    1. From a single stationary device to multiple mobile devices and connections.
    2. Pervasive encryption.
    3. Shift in domain name lookup.
  • Non-ISPs Often Have Access to More and a Wider Range of User Information than ISPs:
    1. Non-ISP services have unique insights into user activity.
      • social networks
      • search engines
      • webmail and messaging
      • operating systems
      • mobile apps
      • interest-based advertising
      • browsers
      • Internet video
      • e-commerce.
    2. Non-ISPs dominate in cross-context tracking.
    3. Non-ISPs dominate in cross-device tracking.

Table Of Contents

Online Privacy and ISPs: ISP Access to Consumer Data is Limited and Often Less than Access by Others

Summary of Contents:

  • Preface
  • Executive Summary
    • Appendix 1: Some Key Terms
  • Chapter 1: Limited Visibility of Internet Service Providers Into Users’ Internet Activity
    • Appendix 1: Encryption for Top 50 Web Site
    • Appendix 2: The Growing Prevalence of HTTPS as Fraction of Internet Traffic
  • Chapter 2: Social Networks
  • Chapter 3: Search Engines
  • Chapter 4: Webmail and Messaging
  • Chapter 5: How Mobile Is Transforming Operating Systems
  • Chapter 6: Interest-Based Advertising (“IBA”) and Tracking
  • Chapter 7: Browsers, Internet Video, and E-commerce
  • Chapter 8: Cross-Context Tracking
    • Appendix 1: Cross-Context Chart Citations
  • Chapter 9: Cross-Device Tracking
  • Chapter 10: Conclusion

Mentions

  • HTTPS
  • Interest-Based Advertising (IBA)
  • Tracking
  • Location
    • Coarse Location
    • Precise Location
  • Natural Language Conversation Robots (a.k.a. ‘bots)
    • Siri, Apple
    • Now, Google Now
    • Cortana, Microsoft

Argot

Also see page 124 of The Work.

  • Availability → contra Use
  • Big Data → data which is very big.
  • Broadband Internet Access Services → an ISP, but not a dialup service
    as used in the Open Internet Order, of the FCC, 2015-24, Appendix A.
  • Chat bot → <fancy>Personal Digital Assistance</fancy>
  • Cookie
  • CPNI → Customer Proprietary Network Information
    47 U.S.C. §222. Also, Section 222 are at 47 C.F.R.§ 64.2001 et seq.
  • Cross-Dontext
  • Cross-Device
  • DNS → Domain Name Service
  • DPI → Deep Packet Inspection
  • Edge Providers → smart pipes, page stuffing, click-baiting; e.g. Akamai, CloudFlare, CloudFront, etc.. exemplars.
  • End-to-End
    • Argument
    • Encryption
  • Factual Analysis → this means something different to lawyers contra engineers.
  • FCC → Federal Communications Commission
  • Form
    Form Autofill, a browser feature
  • FTC → Federal Trade Commission
  • FTT → Freedom To Tinker, a venue, an oped
  • GPS → Global Positioning System
  • HTTP → you know.
  • HTTPS → you know.
  • IBA → Interest-Based Advertising
  • IP → Internet Protocol
    • Address
  • IoT → Internet of Thingies Toys Unpatchables
  • IRL → <culture who=”The Youngs”>In Real Life</culture>
  • ISP → Internet Service Provider
  • Last Mile, of an ISP
  • Location
    • Coarse → “city”- “DMA”- or “country”-level
    • Precise → an in-industry definition exists
  • Metadata → indeed.
  • OBA → Online Behavioral Advertising
  • Open Internet Order, of the FCC.
  • OS → <ahem>Operating System</ahem>
  • Party System
    • First Party
    • [Second Party], no one cares.
    • Third Party
    • [Fourth Party]
  • Personal Information → the sacred stuff, the poisonous stuff
  • Personal Digital Assistant → a trade euphemism for NLP + command patterns for IVR; all the 1st-tier shops have one nowadays.
    • Siri → Apple
    • Now → Google
    • Cortana → Microsoft
  • Scanning
  • Section 222, see Title II
  • SSL → you mean TLS
  • Title II, of the Telecommunications Act.
    • Section 222,
  • Tracking
    • (Across-) Cross-Context
    • (Across-) Cross-Device
  • TLS → you mean SSL
  • UGC → User-Generated Content (unsupervised filth; e.g. comment spam)
  • URL → you know.
  • VPN → run one.
  • WiFi → for some cultural reason “wireless” turns into “Wireless Fidelity” and “WiFi”
  • Working Paper → are unreviewed work products..
  • Visibility → bookkeeping by the surveillor observer.

Actualities

References

Of course, it’s a legal-style policy whitepaper. Of course there are references; they are among the NN footnotes. In rough order of appearance in the work.

 

Trajectory Recovery from Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data | Xu, Tu, Li, Zhang, Fu, Jin

Fengli Xu, Zhen Tu, Yong Li, Pengyu Zhang, Xiaoming Fu, Depeng Jin; Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data; In Proceedings of the Conference on the World Wide Web (WWW); 2017-02-21 (2017-02-25); 10 pages; arXiv:1702.06270

tl;dr → probabilistic individuation from timestamped aggregated population location records.

Abstract

Human mobility data has been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual’s mobility records usually gives rise to privacy issues, datasets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at a specific timestamp, which is believed to be sufficient for preserving users’ privacy. However, in this paper, we argue and prove that even publishing aggregated mobility data could lead to privacy breach in individuals’ trajectories. We develop an attack system that is able to exploit the uniqueness and regularity of human mobility to recover individual’s trajectories from the aggregated mobility data without any prior knowledge. By conducting experiments on two real-world datasets collected from both mobile application and cellular network, we reveal that the attack system is able to recover users’ trajectories with accuracy about 73%~91% at the scale of tens of thousands to hundreds of thousands users, which indicates severe privacy leakage in such datasets. Through the investigation on aggregated mobility data, our work recognizes a novel privacy problem in publishing statistic data, which appeals for immediate attentions from both academy and industry.

Promotions

References

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  2. Apple’s commitment to your privacy.
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  4. G. Acs and C. Castelluccia. A case study: privacy preserving release of spatio-temporal density in Paris. In Proceedings of the ACM Conference of the Special Interest Group on Knowledge D-something and D-Something (SIGKDD). ACM, 2014.
  5. China telcom’s big data products.
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  9. M. Seshadri, S. Machiraju, A. Sridharan, et al. Mobile call graphs: beyond power-law and lognormal distributions. In Proceedings of the ACM Conference on Knowledge Discovery? and Discernment? (KDD). ACM, 2008.
  10. Y. Wang, H. Zang, M. Faloutsos. Inferring cellular user demographic information using homophily on call graphs. In Proceedings of the IEEE Workshop on Computer Communications (INFOCOM) IEEE, 2013.
  11. A. Wesolowski, N. Eagle, A. J. Tatem, et al. Quantifying the impact of human mobility on malaria. In Science, 2012.
  12. M. Saravanan, P. Karthikeyan, A. Aarthi. Exploring community structure to understand disease spread and control using mobile call detail records. NetMob D4D Challenge, 2013. Probably there’s a promotional micro-site for this.
  13. R. W. Douglass, D. A. Meyer, M. Ram, et al. High resolution population estimates from telecommunications data. In EPJ Data Science, 2015.
  14. H. Wang, F. Xu, Y. Li, et al. Understanding mobile traffic patterns of large scale cellular towers in urban environment. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2015.
  15. L. Sweeney. k-anonymity: A model for protecting privacy. In International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2002.
  16. Y. de Montjoye, L. Radaelli, V. K. Singh, et al. Unique in the shopping mall: On the reidentifiability of credit card metadata. In Science, 2015.
  17. H. Zang and J. Bolot. Anonymization of location data does not work: A large-scale measurement study. In Proceedings of the ACM Conference on Mobile Communications (Mobicom). ACM, 2011.
  18. M. Gramaglia and M. Fiore. Hiding mobile traffic fingerprints with glove. In Proceedings of the ACM Conference CoNEXT, 2015.
  19. A.-L. Barabasi. The origin of bursts and heavy tails in human dynamics. In Nature, 2005.
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  21. Y. de Montjoye, C. A. Hidalgo, M. Verleysen, et al. Unique in the crowd: The privacy bounds of human mobility. In Scientific Reports, 2013.
  22. G. B. Dantzig. Linear Programming and Extensions. Princeton University Press, 1998.
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  24. O. Abul, F. Bonchi, M. Nanni. Anonymization of moving objects databases by clustering and perturbation. In Information Systems, 2010.
  25. Pascal Welke, Ionut Andone, Konrad Blaszkiewicz, Alexander Markowetz. Differentiating smartphone users by app usage. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 519–523. ACM, 2016.
  26. Lukasz Olejnik, Claude Castelluccia, Artur Janc. Why Johnny Can’t Browse in Peace: On the uniqueness of web browsing history patterns. In Proceedings of the 5th Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETs), 2012.
  27. M. C. Gonzalez, C. A. Hidalgo, A.-L. Barabasi. Understanding individual human mobility patterns. In Nature, 2008.
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  29. Y. Liu, K. P. Gummadi, B. Krishnamurthy, et al. Analyzing Facebook Privacy Settings: User Expectations vs. Reality. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2011.
  30. B. Krishnamurthy and C. E. Wills. Generating a privacy footprint on the Internet. In Proceedings of the ACM Internet Measurement Conference
  31. S. Le B., C. Zhang, A. Legout, et al. I know where you are and what you are sharing: exploiting P2P communications to invade users’ privacy. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2011.
  32. S. Liu, I. Foster, S. Savage, et al. Who is. com? learning to parse WHOIS records. In Proceedings of the ACM Internet Measurement Conference (IMC). ACM, 2015.
  33. H. Kido, Y. Yanagisawa, T. Satoh. Protection of location privacy using dummies for location-based services. In Proceedings of the IEEE International Conference on (Mountain?) DEW (ICDEW). IEEE, 2005.
  34. A. Monreale, G. L. Andrienko, N. V. Andrienko, et al. Movement data anonymity through generalization. In Transactions on Data Privacy, 2010.
  35. K. Sui, Y. Zhao, D. Liu, et al. Your trajectory privacy can be breached even if you walk in groups. In Proceedings of the IEEE/ACM International Workshop on Quality of Service (IWQoS), 2016.
  36. Y. Song, D. Dahlmeier, S. Bressan. Not so unique in the crowd: a simple and effective algorithm for anonymizing location data. In PIR@ SIGIR, 2014.
  37. S. Garfinkel. Privacy protection and RFID. In Ubiquitous and Pervasive Commerce. Springer, 2006.
  38. J. Domingo-Ferrer and R. Trujillo-Rasua. Microaggregation-and permutation-based anonymization of movement data. In Information Sciences, 2012.
  39. Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, Salil Vadhan. Robust Traceability From Trace Amounts. In Proceedings of the 56th Annual IEEE Symposium on Foundations of Computer Science (FOCS), , pages 650–669. IEEE, 2015.

Previously filled.

(Cross-)Browser Fingerprinting via OS and Hardware Level Features | Cao, Song, Wijmans

Yinzhi Cao, Song Li, Erik Wijmans; (Cross-)Browser Fingerprinting via OS and Hardware Level Features; In Proceedings of the Network & Distributed System Security Symposium (NSDI); 2017-02-26; 15 pages.

Abstract

In this paper, we propose a browser fingerprinting technique that can track users not only within a single browser but also across different browsers on the same machine. Specifically, our approach utilizes many novel OS and hardware level features, such as those from graphics cards, CPU, and installed writing scripts. We extract these features by asking browsers to perform tasks that rely on corresponding OS and hardware functionalities.

Our evaluation shows that our approach can successfully identify 99.24% of users as opposed to 90.84% for state of the art on single-browser fingerprinting against the same dataset. Further, our approach can achieve higher uniqueness rate than the only cross-browser approach in the literature with similar stability.

Mentions

Browsers

  • Chrome
  • Edge
  • Firefox
  • Internet Explorer
  • Opera
  • Safari
  • Other
    • Maxthon
    • Tor
    • UC

Population

  • Amazon Mechanical Turk
  • MacroWorkers

Others

  • AmIUnique
  • Panopticlick
  • Boda

Actualities

Who

Yinzhi Cao, Assistant Professor, Computer Science and Engineering Department, Lehigh University.

Promotions

New Fingerprinting Techniques Identify Users Across Different Browsers on the Same PC; ; In BleepingComputer; 2017-01-12.

References

  • Core estimator.
  • [email threads] proposal: navigator.cores; InArchives of WhatWG of the W3C, circa 2014-05.
  • Am I Unique?, at GitHub.
  • anti-aliasing, at Graphics Wikia.
  • Panopticlick: Is your browser safe against tracking?
  • Watched; Wall Street Journal (WSJ).
  • cube mapping; In Jimi Wales’ Wiki.
  • list of writing systems; In Jimi Wales’ Wiki.
  • G. Acar, C. Eubank, S. Englehardt, M. Juarez, A. Narayanan, C. Diaz; “The web never forgets: Persistent tracking mechanisms in the wild,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security (CCS ’14), 2014, pp. 674–689.
  • G. Acar, M. Juarez, N. Nikiforakis, C. Diaz, S. Gürses, F. Piessens, B. Preneel; “FPDetective: Dusting the web for fingerprinters,” in Proceedings of the 2013 ACM SIGSAC Conference on Computer and Communications Security (CCS ’13), 2013, pp. 1129–1140.
  • M. Ayenson, D. Wambach, A. Soltani, N. Good, C. Hoofnagle; “Flash cookies and privacy II: Now with HTML5 and ETag respawning,” Available at SSRN 1898390, 2011.
  • S. Berger. You should install two browsers.
  • T. Bigelajzen. Cross browser zoom and pixel ratio detector.
  • K. Boda, A. M. F ̈oldes, G. G. Gulyás, S. Imre, “User tracking on the web via cross-browser fingerprinting,” in Proceedings of the 16th Nordic Conference on Information Security Technology for Applications, (NordSec’11), 2012, pp. 31–46.
  • F. Boesch. Soft shadow mapping.
  • Federal Trade Commission (FTC). Cross-device tracking. A celebration. 2015-11.
  • P. Eckersley, “How unique is your web browser?” in Proceedings of the 10th International Conference on Privacy Enhancing Technologies (PETS’10), 2010.
  • S. Englehardt A. Narayanan, “Online tracking: A 1-million-site measurement and analysis,” in Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security, (CCS ’16), 2016.
  • A. Etienne J. Etienne. Classical suzanne monkey from blender to get your game started with threex.suzanne
  • D. Fifield S. Egelman, “Fingerprinting web users through font metrics,” in Financial Cryptography and Data Security. Springer, 2015, pp. 107–124.
  • S. Kamkar. Evercookie.
  • B. Krishnamurthy, K. Naryshkin, C. Wills, “Privacy leakage vs. protection measures: the growing disconnect,” in Web 2.0 Security and Privacy Workshop, 2011.
  • B. Krishnamurthy C. Wills, “Privacy diffusion on the web: a longitudinal perspective,” in Proceedings of the 18th International Conference on World Wide Web (WWW). ACM, 2009, pp. 541–550.
  • B. Krishnamurthy C. E. Wills. “Generating a privacy footprint on the internet,” in Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement (IM). ACM, 2006, pp. 65–70.
  • B. Krishnamurthy C. E. Wills. “Characterizing privacy in online social networks,” in Proceedings of the First Workshop on Online Social Networks. ACM, 2008, pp. 37–42.
  • P. Laperdrix, W. Rudametkin, B. Baudry, “Beauty and the beast: Diverting modern web browsers to build unique browser fingerprints”, in Proceedings of the 37th IEEE Symposium on Security and Privacy (S&P 2016), 2016.
  • A. Lerner, A. K. Simpson, T. Kohno, F. Roesner, “Internet jones and the raiders of the lost trackers: An archaeological study of web tracking from 1996 to 2016,” in Proceedings of the 25th USENIX Security Symposium (USENIX Security 16), Austin, TX, 2016.
  • J. R. Mayer J. C. Mitchell, “Third-party web tracking: Policy and technology,” in Proceedings of the 2012 IEEE Symposium on Security and Privacy (SP), 2012, pp. 413–427.
  • W. Meng, B. Lee, X. Xing, W. Lee, “Trackmeornot: Enabling flexible control on web tracking,” in Proceedings of the 25th International Conference on World Wide Web (WWW ’16), 2016, pp. 99–109.
  • H. Metwalley, S. Traverso, “Unsupervised detection of web track- ers,” in Globecom, 2015.
  • K. Mowery, D. Bogenreif, S. Yilek, H. Shacham, “Fingerprinting information in javascript implementations,” 2011.
  • K. Mowery, H. Shacham, “Pixel perfect: Fingerprinting canvas in HTML5,” In Some Venue, 2012.
  • M. Mulazzani, P. Reschl, M. Huber, M. Leithner, S. Schrittwieser, E. Weippl, F. Wien, “Fast and reliable browser identification with javascript engine fingerprinting,” in Proceedings of W2SP, 2013.
  • G. Nakibly, G. Shelef, S. Yudilevich, “Hardware fingerprinting using HTML5,” arXiv preprint arXiv:1503.01408, 2015.
  • N. Nikiforakis, W. Joosen, B. Livshits, “Privaricator: Deceiving fingerprinters with little white lies,” in Proceedings of the 24th International Conference on World Wide Web, (WWW ’15), 2015, pp. 820–830.
  • N. Nikiforakis, A. Kapravelos, W. Joosen, C. Kruegel, F. Piessens, G. Vigna, “Cookieless monster: Exploring the ecosystem of web-based device fingerprinting,” in In Proceedings of the IEEE Symposium on Security and Privacy (SP), 2013.
  • X. Pan, Y. Cao, Y. Chen, “I do not know what you visited last summer – protecting users from third-party web tracking with trackingfree browser,” in Proceedings of the Network & Distributed Systems Symposium (NDSS), 2015.
  • M. Perry, E. Clark, S. Murdoch, “The design and implementation of the Tor Browser [draft][online], United States,” 2015.
  • F. Roesner, T. Kohno, D. Wetherall, “Detecting and defending against third-party tracking on the web,” in Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation (NSDI’12), 2012, pp. 12–12.
  • I. Sánchez-Rola, X. Ugarte-Pedrero, I. Santos, P. G. Bringas “Tracking users like there is no tomorrow: Privacy on the current internet,” in International Joint Conference,/em>. Springer, 2015, pp. 473– 483.
  • A. Soltani, S. Canty, Q. Mayo, L. Thomas, C. J. Hoofnagle. “Flash cookies and privacy,” in Proceedings of the AAAI Spring Symposium: Intelligent Information Privacy Management,/em>, 2010.
  • US-CERT. Securing your web browser.
  • Do Not Track Policy. In Jimi Wales’ Wiki.
  • Privacy Mode
  • M. Xu, Y. Jang, X. Xing, T. Kim, W. Lee, “Ucognito: Private browsing without tears,” in Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security (CCS ’15), 2015, pp. 438–449.
  • T.-F. Yen, Y. Xie, F. Yu, R. P. Yu, M. Abadi, “Host fingerprinting and tracking on the web: Privacy and security implications,” in Proceedings of the Network & Distributed Systems Symposium (NDSS), 2012.

Smart TV (Fall Technology Series) | FTC

Smart TV; Federal Trade Commission (FTC); 2016-12-07.

Mentions

Surely they said something of import.

Who

  • Justin Brookman is Policy Director of the FTC’s Office of Technology Research and Investigation (OTECH)
  • Ian Klein is a graduate student pursuing an MS in Computer Science at Stevens Institute of Technology,
  • Josh Chasin is the Chief Research Officer of comScore.
  • Jane Clarke is the CEO and Managing Director of the Coalition for Innovative Media Measurement (CIMM).
  • Shaq Katikala is Counsel and Assistant Director of Technology & Data Science at the Network Advertising Initiative (NAI).
  • Ashwin Navin is CEO and co-founder of Samba TV.
  • Mark Risis was the Head of Strategy and Business Development for TiVo Research through 2016-11.
  • Serge Egelman is the Research Director of the Usable Security & Privacy Group at the International Computer Science Institute (ICSI), is “lead” at the Berkeley Laboratory for Usable and Experimental Security at the University of California, Berkeley.
  • Claire Gartland is Director of the Consumer Privacy Project at the Electronic Privacy Information Center (EPIC).
  • Dallas Harris is a Policy Fellow at Public Knowledge.
  • Emmett O’Keefe is Senior Vice President of Advocacy at the Direct Marketing Association (DMA)
  • Maria Rerecich is the Director of Electronics Testing at Consumer Reports (CR).

FreeSense:Indoor Human Identification with WiFi Signals | Xin, Guo, Wang, Li, Yu

Tong Xin, Bin Guo, Zhu Wang, Mingyang Li, Zhiwen Yu; FreeSense:Indoor Human Identification with WiFi Signals; 2016-08-11; arxiv:1608.03430.

Abstract

Human identification plays an important role in human-computer interaction. There have been numerous methods proposed for human identification (e.g., face recognition, gait recognition, fingerprint identification, etc.). While these methods could be very useful under different conditions, they also suffer from certain shortcomings (e.g., user privacy, sensing coverage range). In this paper, we propose a novel approach for human identification, which leverages WIFI signals to enable non-intrusive human identification in domestic environments. It is based on the observation that each person has specific influence patterns to the surrounding WIFI signal while moving indoors, regarding their body shape characteristics and motion patterns. The influence can be captured by the Channel State Information (CSI) time series of WIFI. Specifically, a combination of Principal Component Analysis (PCA), Discrete Wavelet Transform (DWT) and Dynamic Time Warping (DTW) techniques is used for CSI waveform-based human identification. We implemented the system in a 6m*5m smart home environment and recruited 9 users for data collection and evaluation. Experimental results indicate that the identification accuracy is about 88.9% to 94.5% when the candidate user set changes from 6 to 2, showing that the proposed human identification method is effective in domestic environments.

Living on Fumes: Digital Footprints, Data Fumes, and the Limitations of Spatial Big Data | Jim Thatcher

Jim Thatcher (Clark University); Living on Fumes: Digital Footprints, Data Fumes, and the Limitations of Spatial Big Data; In International Journal of Communications (IJC); Volume 8; 2014; 19 pages; landing; previously in Proceedings of the 26th International
Cartographic Conference (ICC), 2014.

tl;dr → whereas capitalism is bad, the critical theory: sociotechnical, epistemic project, abductive processes, epistemic limits, epistemic and ontological commitments, capitalist profit motives, private corporations; frameworks of Marcuse, Pickles. You get the idea.

Abstract

Amid the continued rise of big data in both the public and private sectors, spatial information has come to play an increasingly prominent role. This article defines big data as both a sociotechnical and epistemic project with regard to spatial information. Through interviews, job shadowing, and a review of current literature, both academic researchers and private companies are shown to approach spatial big data sets in analogous ways. Digital footprints and data fumes, respectively, describe a process that inscribes certain meaning into quantified spatial information. Social and economic limitations of this data are presented. Finally, the field of geographic information science is presented as a useful guide in dealing with the “hard work of theory” necessary in the big data movement.

Mentions

  • In the introductory paragraph, cites opinements in Fast Company and Mashable as authoritative directional indicators.
  • Two problems
    1. <quote>On the one hand, rather than fully capturing life as researchers hope, end-user interactions within big data are necessarily the result of decisions made by an extremely small group of programmers working for private corporations that have [been] promulgated through the mobile application ecosystem.
    2. On the other hand, in accepting that the data gathered through mobile applications reveal meaningful information about the world, researchers are tacitly accepting a commodification and quantification of knowledge.</quote>
  • Big Data is
    • (wait for it …) very big, “large” even.
    • <quote>data whose size forces us to look beyond the tried-and-true methods
      that are prevalent at that time</quote>, Adam Jacobs.
    • Contrarianism
      • Something vague about Taylorism, Max Weber, etc.
      • Something vague about how having more data is better, or is not better.
    • The Fourth Paradigm
      1. empiricism
      2. analysis
      3. simulation.
      4. explore & exploit
    • Sources
      <quote>Most current studies describing themselves as “big data” with a spatial component revolve around two mobile software platforms [Foursquare, Twitter]</quote>

      • Foursquare
      • Twitter
      • Facebook
      • Flickr
  • Types of Data [plural of types of Datum(s)]
    • Checkin
    • Tweet
  • Livehood
  • 25% of Foursquare users link their Twitter accounts (75% don’t)
  • <quote>Finally, the reliance upon data generated with an explicit motive for profit — both for the end user and the corporation—results in epistemological commitments not dissimilar to concerns raised with regard to the knowledges and approaches privileged by GIS use. </quote>
  • <quote>This hard work of theory opens new knowledge projects within the realm of big data. For example, if the check-in is viewed as a form of disciplining technology — one that reports location to enmesh it more fully in capitalist exchange — then purposeful location fraud takes on new meaning as a potential form of resistance or protest.</quote>

Badness

  • private companies
  • profit motives
  • capitalism

Metaphors

  • Digital footprints
  • Digital fumes

Technologies

  • PostgreSQL
  • R
  • Mac (OS)

References

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  • Baker, S. (2012-01-05). Can social media sell soap? The New York Times.
  • Batty, M. (2012). Smart cities, big data. Environment and Planning B, 39, 191–193.
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  • Berry, D. M. (2011). The philosophy of software: Code and mediation in the digital age. London, UK: Palgrave Macmillan.
  • boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662–679.
  • Brownlee, J. (2012-03-30). This creepy app isn’t just stalking women without their knowledge, it’s a wake-up call about Facebook privacy. In Cult of Mac.
  • Burgess, J., & Bruns, A. (2012). Twitter archives and the challenges of “big social data” for media and communication research. M/C Journal, 15(5).
  • Carbunar, B., & Potharaju, R. (2012). You unlocked the Mt. Everest badge on Foursquare! Countering location fraud in geosocial networks. In Proceedings of the 2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS), pages 182-190. IEEE Computer Society, Washington, DC.
  • Cerrato, P. (2012-11-01). Big data analytics: Where’s the ROI? InformationWeek: Healthcare.
  • Cheng, Z., Caverlee, J., Lee, K., & Sui, D. (2011). Exploring millions of footprints in location sharing services. In Proceedings of the Fifth International AAAI Conference on WSM. Barcelona, Spain.
  • Crampton, J. (2003). The political mapping of cyberspace. Edinburgh, Scotland: Edinburgh University Press.
  • Crampton, J. (2013). Commentary: Is security sustainable? Environment and Planning D, 31, 571–577.
  • Cranshaw, J., Schwartz, R., Hong, J., & Sadeh, N. (2012). The Livehoods Project: Utilizing social media to understand the dynamics of a city. In Proceedings of the Sixth International AAAI Conference on WSM. Dublin, Ireland.
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Actualities

Via: backfill.